DNA promises to be a high density data storage medium, but physical storage poses a challenge. To store large amounts of data, pools must be physically isolated so they can share the same addressing scheme. We propose the storage of dehydrated DNA spots on glass as an approach for scalable DNA data storage. The dried spots can then be retrieved by a water droplet using a digital microfluidic device. Here we show that this storage schema works with varying spot organization, spotted masses of DNA, and droplet retrieval dwell times. In all cases, the majority of the DNA was retrieved and successfully sequenced. We demonstrate that the spots can be densely arranged on a microfluidic device without significant contamination of the retrieval. We also demonstrate that 1 TB of data could be stored in a single spot of DNA and successfully retrieved using this method.
Microfluidic devices promise to automate wetlab procedures by manipulating small chemical or biological samples. This technology comes in many varieties, all of which aim to save time, labor, and supplies by performing lab protocol steps typically done by a technician. However, existing microfluidic platforms remain some combination of inflexible, error-prone, prohibitively expensive, and difficult to program. We address these concerns with a full-stack digital microfluidic automation platform. Our main contribution is a runtime system that provides a high-level API for microfluidic manipulations. It manages fluidic resources dynamically, allowing programmers to freely mix regular computation with microfluidics, which results in more expressive programs than previous work. It also provides real-time error correction through a computer vision system, allowing robust execution on cheaper microfluidic hardware. We implement our stack on top of a low-cost droplet microfluidic device that we have developed. We evaluate our system with the fully-automated execution of polymerase chain reaction (PCR) and a DNA sequencing preparation protocol. These protocols demonstrate high-level programs that combine computational and fluidic operations such as input/output of reagents, heating of samples, and data analysis. We also evaluate the impact of automatic error correction on our system's reliability.
We see frequent headlines about distributed denial of service (DDoS) of ever greater volumes. But does this reflect a true image of the DDoS problem? We can look at recent history to gain some insights into this obsession with attack size.
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