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Full-Text Articles in Biological Engineering
Arakniprint: 3d Printing Of Synthetic Spider Silk To Produce Biocompatible And Resorbable Biomaterials, Ashley Ruben, Brianne Bell, Chase Spencer, Craig Soelberg, Dan Gil, Thomas Harris, Richard Decker, Timothy A. Taylor, Randolph V. Lewis
Arakniprint: 3d Printing Of Synthetic Spider Silk To Produce Biocompatible And Resorbable Biomaterials, Ashley Ruben, Brianne Bell, Chase Spencer, Craig Soelberg, Dan Gil, Thomas Harris, Richard Decker, Timothy A. Taylor, Randolph V. Lewis
Student Research Symposium
At $3.07 billion in 2013, the 3D printing industry was projected to reach $12.8 billion in 2018 and exceed $21 billion by 2020 (Wohlers and Caffrey, 2013). A lucrative part of this expanding industry includes printing biocompatible medical implants, devices, and tissue scaffolds. A common problem encountered with traditional devices, implants, and tissue scaffolds is that they are not unique to the patient and lack the necessary strength and biocompatibility. To answer these demands, customizable devices are being produced from patient medical scans and CAD designs using 3D printers. These printers traditionally use thermoplastics because of the ease with which …
Secretion Of Bioplastic Polymers From Methanotrophic Bacteria Grown Using Natural Gas, Chad L. Nielsen, Charles D. Miller
Secretion Of Bioplastic Polymers From Methanotrophic Bacteria Grown Using Natural Gas, Chad L. Nielsen, Charles D. Miller
Student Research Symposium
Biodegradable bioplastics show promise as a replacement for traditional plastics. Cost of production due to the cost of feedstocks and separation/purification processes are the main obstacles to widespread use of bioplastics. The possibility of reducing these costs through using methane gas as a feedstock and genetically transforming a methanotrophic bacterium to secrete bioplastics was investigated through experimentation. The bacteria are a promising option for bioplastic production.
Computational Capabilities Of Leaky Integrate-And-Fire Neural Networks For Liquid State Machines, Amin Almassian, Christof Teuscher
Computational Capabilities Of Leaky Integrate-And-Fire Neural Networks For Liquid State Machines, Amin Almassian, Christof Teuscher
Student Research Symposium
We analyze the computational capability of Leaky Integrate-and-Fire (LIF) Neural Networks used as a reservoir (liquid) in the framework of Liquid State Machines (LSM). Maass et. al. investigated LIF neurons in LSM and their results showed that they are capable of noise-robust, parallel, and real-time computation. However, it still remains an open question how the network topology affects the computational capability of a reservoir. To address that question, we investigate the performance of the reservoir as a function of the average reservoir connectivity. We also show that the dynamics of the LIF reservoir is sensitive to changes in the average …