thermosulfidooxidans upon the addition of DSF family signal compounds. Biofilm dispersal was confirmed to occur in batch cultures of L. The respective signal compounds are known as biofilm dispersal agents. ferriphilum genome revealed the presence of a diffusible soluble factor (DSF) family quorum sensing system. Deep neural networks were also applied to analyze biofilms of different microbial consortia. ferriphilum cells to colonize pyrite and chalcopyrite surfaces and indicated that biofilm dispersal occurs in mature pyrite batch cultures of this species. The method confirmed the high affinity of L. The method was validated by quantifying cell attachment on pyrite and chalcopyrite surfaces with axenic cultures of Acidithiobacillus caldus, Leptospirillum ferriphilum, and Sulfobacillus thermosulfidooxidans. In this study, we used a method for direct quantification of the mineral-attached cell population on pyrite or chalcopyrite particles in bioleaching experiments by coupling high-throughput, automated epifluorescence microscopy imaging of mineral particles with algorithms for image analysis and cell quantification, thus avoiding human bias in cell counting. Biofilm formation is necessary for seeding and persistence of the active microbial community in industrial biomining heaps and tank reactors, and it enhances metal release. Cell attachment on metal sulfides is important for this process. We discuss how difficult real-world challenges faced by image informatics and personalized medicine are being tackled with open-source biomedical data and software.Industrial biomining processes are currently focused on metal sulfides and their dissolution, which is catalyzed by acidophilic iron(II)- and/or sulfur-oxidizing microorganisms. Examples will be covered using existing open-source software tools such as ImageJ, CellProfiler, and IPython Notebook. We present high level discussions of sample preparation and image acquisition data formats storage and databases image processing computer vision and machine learning and visualization and interactive programming. This review targets biomedical scientists interested in getting started on tackling image analytics. Reliable data analytics products require as much automation as possible, which is a difficulty for data like histopathology and radiology images because we require highly trained expert physicians to interpret the information. One of the daunting challenges is how to effectively utilize medical image data in personalized medicine. Patient health data is computationally profiled against a large of pool of feature-rich data from other patients to ideally optimize how a physician chooses care. An area ready to take advantage of these developments is personalized medicine, the concept where the goal is tailor healthcare to the individual. There is a marked increase in image informatics applications as there have been simultaneous advances in imaging platforms, data availability due to social media, and big data analytics. Scenes, what an image contains, come from many imager devices such as consumer electronics, medical imaging systems, 3D laser scanners, microscopes, or satellites. Image informatics encompasses the concept of extracting and quantifying information contained in image data.
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