Generative adversarial networks for augmenting training data of microscopic cell images

Generative adversarial networks (GANs) have recently been successfully used to create realistic synthetic microscopy cell images in 2D and predict intermediate cell stages. In the current paper we highlight that GANs can not only be used for creating synthetic cell images optimized for different fluorescent molecular labels, but that by using GANs for augmentation of training data involving scaling or other transformations the inherent length scale of biological structures is retained. In addition, GANs make it possible to create synthetic cells with specific shape features, which can be used, for example, to validate different methods for feature extraction. Here, we apply GANs to create 2D distributions of fluorescent markers for F-actin in the cell cortex of Dictyostelium cells (ABD), a membrane receptor (cAR1), and a cortex-membrane linker protein (TalA). The recent more widespread use of 3D lightsheet microscopy, where obtaining sufficient training data is considerably more difficult than in 2D, creates significant demand for novel approaches to data augmentation. We show that it is possible to directly generate synthetic 3D cell images using GANs, but limitations are excessive training times, dependence on high-quality segmentations of 3D images, and that the number of z-slices cannot be freely adjusted without retraining the network. We demonstrate that in the case of molecular labels that are highly correlated with cell shape, like F-actin in our example, 2D GANs can be used efficiently to create pseudo-3D synthetic cell data from individually generated 2D slices. Because high quality segmented 2D cell data are more readily available, this is an attractive alternative to using less efficient 3D networks

Capturing the fast-food landscape in England using large-scale network analysis

Fast-food outlets play a significant role in the nutrition of British children who get more food from such shops than the school canteen. To reduce young people’s access to fast-food meals during the school day, many British cities are implementing zoning policies. For instance, cities can create buffers around schools, and some have used 200 meters buffers while others used 400 meters. But how close is too close? Using the road network is needed to precisely computing the distance between fast-food outlets (for policies limiting the concentration), or fast-food outlets and the closest school (for policies using buffers). This estimates how much of the fast-food landscape could be affected by a policy, and complementary analyses of food utilization can later translate the estimate into changes on childhood nutrition and obesity. Network analyses of retail and urban forms are typically limited to the scale of a city. However, to design national zoning policies, we need to perform this analysis at a national scale. Our study is the first to perform a nation-wide analysis, by linking large datasets (e.g., all roads, fast-food outlets and schools) and performing the analysis over a high performance computing cluster. We found a strong spatial clustering of fast-food outlets (with 80% of outlets being within 120 of another outlet), but much less clustering for schools. Results depend on whether we use the road network on the Euclidean distance (i.e. ‘as the crow flies’): for instance, half of the fast-food outlets are found within 240 m of a school using an Euclidean distance, but only one-third at the same distance with the road network. Our findings are consistent across levels of deprivation, which is important to set equitable national policies. In line with previous studies (at the city scale rather than national scale), we also examined the relation between centrality and outlets, as a potential target for policies, but we found no correlation when using closeness or betweenness centrality with either the Spearman or Pearson correlation methods

Ostre krwawienia z górnego odcinka przewodu pokarmowego o etiologii nieżylakowej: epidemiologia, etiologia i wyniki leczenia w Polsce w 2014 roku

Ostre krwawienia z górnego odcinka przewodu pokarmowego (GOPP) stanowią najczęstszy stan nagły w gastroenterologii. Na świecie występują z szacowaną roczną częstością wynoszącą 40/150 przypadków na 100 000 ludności. Większość z nich to krwawienia o tak zwanej etiologii nieżylakowej. W 16–20% przypadków w trakcie diagnostyki endoskopowej identyfikuje się więcej niż jedno źródło krwawienia, natomiast w 7–25% przypadków nie udaje się uwidocznić miejsca krwawienia w endoskopii. Ostre krwawienia z GOPP o etiologii nieżylakowej wymagają hospitalizacji częściej  niż krwawienia z dolnego odcinka przewodu pokarmowego, ponadto są potencjalną przyczyną zgonów. Leczenie powinno odbywać się w ośrodkach zapewniających odpowiedni sprzęt i przeszkoloną załogę. Celem artykułu jest oszacowanie skali zjawiska krwawień z GOPP w Polsce na podstawie danych pochodzących z Narodowego Funduszu Zdrowia, zebranych w 2014 roku. Omówiona zostanie częstość hospitalizacji, sposoby leczenia, nawrotowość krwawienia, konieczność transfuzji krwi oraz wskaźniki śmiertelności pacjentów

QuimP : analyzing transmembrane signalling in highly deformable cells

Transmembrane signalling plays important physiological roles, with G protein-coupled cell surface receptors being particularly important therapeutic targets. Fluorescent proteins are widely used to study signalling, but analyses of image time series can be challenging, in particular when cells change shape. QuimP software semi-automatically tracks spatio-temporal patterns of fluorescence at the cell membrane at high spatial resolution. This makes it a unique tool for studying transmembrane signalling, particularly during cell migration in immune or cancer cells for example

Data pre-processing

Component contains all files used to clean and prepare data before running simulations (pre-processing

Automatic segmentation of radiographic images in industrial applications

A technology that utilizes penetrating rays is one of the oldest nondestructive testing methods. Nowadays, the process of radiogram analysis is performed by qualified human operators and automatic systems are still under development. In this work we present advanced algorithms for automatic segmentation of radiographic images of welded joints. The goal of segmentation of a radiogram is to change and simplify representation of the image into a form that is more meaningful and easier to analyse automatically. The radiogram is divided into parts containing the weld line, image quality indicators, lead characters, and possible defects. Then, each part is analysed separately by specialized algorithms within the framework of the Intelligent System for Radiogram Analysis

Analyzing, simulating, and visualizing complex social systems

Advisors: Philippe J. Giabbanelli.Committee members: Nicholas Karonis; Jose J. Padilla; Michael Papka.Includes illustrations and maps.Includes bibliographical references.Policy formulation and implementation is a multi-dimensional process, which requires a common platform to build communication between all sides involved. The growing availability of data along with the development of information and communication technology solutions (ICTs) supports this process by providing virtual platforms to design and evaluate policies. This thesis seeks to develop systems for policy-making with an emphasis on exploring and identifying the interacting causes that shape health. Our computational methods are primarily applied to the cause of obesity. In particular, we identify the relationships between fast-food outlets and schools at a national level, whereas it was previously done at a city-level. This thesis goes beyond the development of virtual platforms, by also contributing to newer approaches to analyze their output. Specifically, we develop interactive visualizations to help decision-makers in finding key patterns from large simulations of complex systems. Overall, this work has a few limitations. Despite the wealth and scale of data used in our study, it neither captures every single aspect that drives population health, nor does it track them with high temporal and spatial accuracy. Future work should explore the application of our model as a test platform for possible interventions, for instance through usability studies with policy-makers and an extended cost-benefit analysis of simulation results.M.S. (Master of Science

Cholangiopankreatoskopia z użyciem systemu „SpyGlass” — evidence based medicine

Cholangiopankreatoskop „SpyGlass” znalazł zastosowanie głównie w diagnostyce zwężeń dróg żółciowych o nieokreślonym charakterze. Poza tym jest bardzo przydatny w leczeniu dużych złogów dróg żółciowych nieusuwalnych prostszymi metodami oraz w diagnostyce i leczeniu trzustki poprzez bezpośrdnia wizualizację wnętrza przewodu Wirsunga