Using Machine Learning to Predict Bacterial Growth According to the Media Components
About this episode
Bacterial growth depends on the complex interactions of a multitude of chemical components. Microbiologists have long attempted to predict bacterial growth according to culture media components, and have employed a variety of mathematical and computational models to this end. Dr Bei-Wen Ying and her colleagues at the University of Tsukuba, Japan, successfully applied machine learning to understand the contribution of media culture components to bacterial growth. Their work makes a significant contribution to growth prediction and demonstrates that machine learning can be employed in the exploration of the complex dynamics that regulate living systems.
Original Article Reference
This animation is a summary of https://doi.org/10.1038/s41598-019-43587-8
Most human diseases are localised in terms of their location but currently, injected or orally administered drugs are evenly distributed all over the body and thus, act indiscriminately. The targeted delivery of medication to the exact site where it is needed is a common theme in science fiction but thanks to Professor Richard Klemke and his team at the University of California San Diego’s Moores Cancer Center, this fantasy may soon become a reality.
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Dr Doug Brugge | The Community Assessment of Freeway Exposure and Health Studies: Minimising Exposure to Traffic-related Air Pollution
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Modern microelectronics is currently facing a profound challenge. The demand for even smaller and more closely packed electronics has hit a stumbling block: the power emitted in these devices releases more heat than can be efficiently removed. Now, the Terra Quantum team proposes a solution based on the seemingly counterintuitive phenomenon of ‘negative capacitance’.
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