3D surface reconstruction of cellular cryo soft X ray microscopy tomograms using semisupervised deep learning

Cryo soft X ray tomography cryo SXT is a powerful method to investigate the ultrastructure of cells, offering resolution in the tens of nanometer range and strong contrast for membranous structures without requiring labeling or chemical fixation. The short acquisition time and the relatively large field of view leads to fast acquisition of large amounts of tomographic image data. Segmentation of these data into accessible features is a necessary step in gaining biologically relevant information from cryo soft X ray tomograms. However, manual image segmentation still requires several orders of magnitude more time than data acquisition. To address this challenge, we have here developed an end to end automated 3D segmentation pipeline based on semisupervised deep learning. Our approach is suitable for high throughput analysis of large amounts of tomographic data, while being robust when faced with limited manual annotations and variations in the tomographic conditions. We validate our approach by extracting three dimensional information on cellular ultrastructure and by quantifying nanoscopic morphological parameters of filopodia in mammalian cell

A comparative co simulation analysis to improve the sustainability of cogeneration based district multi energy systems using photovoltaics, power to heat, and heat storage

For an extensive decarbonization of district multi energy systems, efforts are needed that go beyond today s cogeneration of heat and power in district multi energy systems. The multitude of existing technical possibilities are confronted with a large variety of existing multi energy system configurations. The variety impedes the development of universal decarbonization pathways. In order to tackle the decarbonization challenge in existing and distinct districts, this paper calculates a wide range of urban district configurations in an extensive co simulation based on domain specific submodels. A district multi energy system comprising a district heating network, a power grid, and cogeneration is simulated for two locations in Germany with locally captured weather data, and for a whole year with variable parameters to configure a power to heat operation, building insolation refurbishment, rooftop photovoltaic orientation, future energy demand scenarios, and district sizes with a temporal resolution of 60 s, in total 3840 variants. The interdependencies and synergies between the electrical low voltage distribution grid and the district heating network are analysed in terms of efficiency and compliance with network restrictions. Thus, important sector specific simulations of the heat and the electricity sector are combined in a holistic district multi energy system co simulation. The clearly most important impact on emission reduction and fuel consumption is a low heat demand, which can be achieved through thermal refurbishment of buildings. Up to 46 reduction in CO2 emissions are possible using the surplus electricity from photovoltaics for power to heat in combination with central heat storage in the district s combined heat and power plant. Domestic hot water heated by district heating network in combination with power to heat conversion distributed in the district reduces the load on the distribution power grid. Even though the investigated measures already improve the sustainability significantly, providing the energy needed for the production of synthetic fuels remains the crucial challenge on the further path towards net zer