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

Computational and analytical evaluation of the efficiency of using hydrogen as a fuel in an internal combustion engine

There are reasons to look for alternative sources of fuel because of the automobile industry development. Firstly, oil is a mined, not a produced fuel, sooner or later it will run out. According to various statistical sources, known deposits are gradually being depleted. Another important problem is air pollution caused by road transport. Most of the existing cars run on gasoline and diesel engines that burn oil to get the car going. Combustion of hydrocarbons that make up oil releases a large amount of harmful substances, in particular, particulate matter and volatile organic compounds. Using hydrogen as an alternative fuel can solve the problem of finding new oil fields, and also, due to the absence of emissions into the atmosphere, it can solve the problem of air pollution. The article presents a brief history of the development of engine building and a description of hydrogen technologies in engines. The article presents a computational and analytical assessment of the efficiency of using hydrogen fuel in an internal combustion engine in relation to the bus fleet of the Yekaterinburg city. It was found that the conversion of a gasoline engine to hydrogen fuel leads to a decrease in specific fuel consumption by 20% (at the nominal mode). It is shown that the payback period for the transfer of 20 buses to hydrogen is about 8 years. © Published under licence by IOP Publishing Ltd

Comparison of Interactions Between Control and Mutant Macrophages

This paper presents a preliminary study on macrophages migration in Drosophila embryos, comparing two types of cells. The study is carried out by a framework called macrosight which analyses the movement and interaction of migrating macrophages. The framework incorporates a segmentation and tracking algorithm into analysing motion characteristics of cells after contact. In this particular study, the interactions between cells is characterised in the case of control embryos and Shot3 mutants, where the cells have been altered to suppress a specific protein, looking to understand what drives the movement. Statistical significance between control and mutant cells was found when comparing the direction of motion after contact in specific conditions. Such discoveries provide insights for future developments in combining biological experiments to computational analysis

Learning to Extract Motion from Videos in Convolutional Neural Networks

This paper shows how to extract dense optical flow from videos with a convolutional neural network (CNN). The proposed model constitutes a potential building block for deeper architectures to allow using motion without resorting to an external algorithm, \eg for recognition in videos. We derive our network architecture from signal processing principles to provide desired invariances to image contrast, phase and texture. We constrain weights within the network to enforce strict rotation invariance and substantially reduce the number of parameters to learn. We demonstrate end-to-end training on only 8 sequences of the Middlebury dataset, orders of magnitude less than competing CNN-based motion estimation methods, and obtain comparable performance to classical methods on the Middlebury benchmark. Importantly, our method outputs a distributed representation of motion that allows representing multiple, transparent motions, and dynamic textures. Our contributions on network design and rotation invariance offer insights nonspecific to motion estimation

A benchmark for epithelial cell tracking

Segmentation and tracking of epithelial cells in light microscopy (LM) movies of developing tissue is an abundant task in cell- and developmental biology. Epithelial cells are densely packed cells that form a honeycomb-like grid. This dense packing distinguishes membrane-stained epithelial cells from the types of objects recent cell tracking benchmarks have focused on, like cell nuclei and freely moving individual cells. While semi-automated tools for segmentation and tracking of epithelial cells are available to biologists, common tools rely on classical watershed based segmentation and engineered tracking heuristics, and entail a tedious phase of manual curation. However, a different kind of densely packed cell imagery has become a focus of recent computer vision research, namely electron microscopy (EM) images of neurons. In this work we explore the benefits of two recent neuron EM segmentation methods for epithelial cell tracking in light microscopy. In particular we adapt two different deep learning approaches for neuron segmentation, namely Flood Filling Networks and MALA, to epithelial cell tracking. We benchmark these on a dataset of eight movies with up to 200 frames. We compare to Moral Lineage Tracing, a combinatorial optimization approach that recently claimed state of the art results for epithelial cell tracking. Furthermore, we compare to Tissue Analyzer, an off-the-shelf tool used by Biologists that serves as our baseline

From bonito to anchovy: a reconstruction of Turkey’s marine fisheries catches (1950-2010)

Turkey’s marine fisheries catches were estimated for the 1950-2010 time period using a reconstruction approach, which estimated all fisheries removals, including unreported landings, recreational landings and discards.  We added these estimates to the ‘official’ data, as reported in TURKSTAT, which are also available from the United Nation’s Food and Agriculture Organization (FAO).  The total reconstructed catch for the 1950-2010 time period (inclusive of the reported data) is approximately 32 million t, or 74% more than the 18.4 million t of reported data. This added approximately 13.6 million t to the reported data, consisting of 6.9 million t of unreported landings, 2.6 million t of discards, 2.4 million t of recreational catches, and 1.7 million t of subsistence catches.  In 2010, total reported marine landings for Turkey were 445,680 t and the total reconstructed catch was 763,760 t, or 73% more than the reported data.   The main unreported taxon by tonnage was European anchovy (Engraulis encrasicolus) due to its sheer high proportion of catch.  The major reasons for underreporting include a general distrust fishers have towards the taxing system combined with inefficient fisheries monitoring and surveillance capabilities.  Accounting for all fisheries components is crucial in understanding the development of fisheries resources, improving management, and reducing threats to the domestic food security of Turkey

Conductance statistics from a large array of sub-10 nm molecular junctions

Devices made of few molecules constitute the miniaturization limit that both inorganic and organic-based electronics aspire to reach. However, integration of millions of molecular junctions with less than 100 molecules each has been a long technological challenge requiring well controlled nanometric electrodes. Here we report molecular junctions fabricated on a large array of sub-10 nm single crystal Au nanodots electrodes, a new approach that allows us to measure the conductance of up to a million of junctions in a single conducting Atomic Force Microscope (C-AFM) image. We observe two peaks of conductance for alkylthiol molecules. Tunneling decay constant (beta) for alkanethiols, is in the same range as previous studies. Energy position of molecular orbitals, obtained by transient voltage spectroscopy, varies from peak to peak, in correlation with conductance values.Comment: ACS Nano (in press

Optical and Transport Studies of Single Molecule Tunnel junctions based on Self-Assembled Monolayers

We have fabricated a variety of novel molecular tunnel junctions based on self-assembled-monolayers (SAM) of two-component solid-state mixtures of molecular wires (1,4 methane benzene-dithiol; Me-BDT with two thiol anchoring groups), and molecular insulator spacers (1-pentanethiol; PT with one thiol anchoring group) at different concentration ratios, r of wires/spacers, which were sandwiched between two metallic electrodes such as gold and cobalt. FTIR spectroscopy and surface titration were used, respectively to verify the formation of covalent bonds with the electrodes, and obtain the number of active molecular wires in the device. The electrical transport properties of the SAM devices were studied as a function of (i) r-value, (ii) temperatures, and (iii) different electrodes, via the conductance and differential conductance spectra. The measurements were used to analyze the Me-BDT density of states near the electrode Fermi level, and the properties of the interface barriers. We measured the Me-BDT single molecule resistance at low bias and gold electrodes to be 6x10^9 Ohm. We also determine the energy difference, D between the Me-BDT HOMO level and the gold Fermi level to be about 1.8 eV. In addition we also found that the temperature dependence of the SAM devices with r < 10^-4 is much weaker than that of the pure PT device (or r = 0), showing a small interface barrier.Comment: 32 pages 10 fugure

AI-powered transmitted light microscopy for functional analysis of live cells

Transmitted light microscopy can readily visualize the morphology of living cells. Here, we introduce artificial-intelligence-powered transmitted light microscopy (AIM) for subcellular structure identification and labeling-free functional analysis of live cells. AIM provides accurate images of subcellular organelles; allows identification of cellular and functional characteristics (cell type, viability, and maturation stage); and facilitates live cell tracking and multimodality analysis of immune cells in their native form without labeling