An Approximate Maximum Common Subgraph Algorithm for Large Digital Circuits

This paper presents an approximate Maximum Common Subgraph (MCS) algorithm, specifically for directed, cyclic graphs representing digital circuits. \ud Because of the application domain, the graphs have nice properties: they are very sparse; have many different labels; and most vertices have only one predecessor. The algorithm iterates over all vertices once and uses heuristics to find the MCS. It is linear in computational complexity with respect to the size of the graph. Experiments show that very large common subgraphs were found in graphs of up to 200,000 vertices within a few minutes, when a quarter or less of the graphs differ. The variation in run-time and quality of the result is low

Larch Status A

LARCH is a model that is used by the Netherlands Environmental Assessment Agency (PBL) for ex-ante and ex-post evaluations of Dutch nature policies. LARCH generates the potential habitat networks of a species. LARCH will not predict the actual distribution of a specie

LARCH Vogels Nationaal; een expertsysteem voor het beoordelen van de ruimtelijke samenhang en de duurzaamheid van broedvogelpopulaties in Nederland

Het model LARCH is een onderdeel van het kerninstrumentarium van het Natuurplanbureau. Het model voorspelt de kans op duurzaam voorkomen van vooral diersoorten op basis van de ruimtelijke configuratie van leefgebieden en de kwaliteit daarvan. Dit rapport beschrijft het toepasbaar maken van LARCH voor 89 broedvogelsoorten in Nederland, LARCH VOGELS NATIONAAL genaamd. Ten behoeve hiervan is een speciale begroeiingstypenkaart vervaardigd. Het model maakt deel uit van de Natuurplanner van het RIVM. De Natuurplanner integreert kennis over de effecten van de zogenoemde "ver"-thema's (verdroging, verzuring, vermesting) op planten en vegetatie(structuur). Door de uitbreiding met broedvogels kan ook de kennis over versnippering worden meegenomen

ColiCoords: A Python package for the analysis of bacterial fluorescence microscopy data

Single-molecule fluorescence microscopy studies of bacteria provide unique insights into the mechanisms of cellular processes and protein machineries in ways that are unrivalled by any other technique. With the cost of microscopes dropping and the availability of fully automated microscopes, the volume of microscopy data produced has increased tremendously. These developments have moved the bottleneck of throughput from image acquisition and sample preparation to data analysis. Furthermore, requirements for analysis procedures have become more stringent given the demand of various journals to make data and analysis procedures available. To address these issues we have developed a new data analysis package for analysis of fluorescence microscopy data from rod-like cells. Our software ColiCoords structures microscopy data at the single-cell level and implements a coordinate system describing each cell. This allows for the transformation of Cartesian coordinates from transmission light and fluorescence images and single-molecule localization microscopy (SMLM) data to cellular coordinates. Using this transformation, many cells can be combined to increase the statistical power of fluorescence microscopy datasets of any kind. ColiCoords is open source, implemented in the programming language Python, and is extensively documented. This allows for modifications for specific needs or to inspect and publish data analysis procedures. By providing a format that allows for easy sharing of code and associated data, we intend to promote open and reproducible research. The source code and documentation can be found via the project’s GitHub page