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Although NIST has now published the data needed to generate match statistics for NGS-based profiles, other hurdles must still be cleared before the new technology sees widespread use in forensics. For instance, labs will have to develop ways to manage the greater amounts of data produced by NGS. They will also have to implement operating procedures and quality controls for the new technology. Still, while much work remains, said Peter Vallone, the research chemist who leads NIST’s forensic genetics research, “We’re laying the foundation for the future.”


DNA is often considered the most reliable form of forensic evidence, and this reputation is based on the way DNA experts use statistics. When they compare the DNA left at a crime scene with the DNA of a suspect, experts generate statistics that describe how closely those DNA samples match. A jury can then take those match statistics into account when deciding guilt or innocence.

These match statistics are reliable because they’re based on rigorous scientific research. However, that research only applies to DNA fingerprints, also called DNA profiles, that have been generated using current technology. Now, scientists at the National Institute of Standards and Technology (NIST) have laid the statistical foundation for calculating match statistics when using Next Generation Sequencing, or NGS, which produces DNA profiles that can be more useful in solving some crimes. This research, which was jointly funded by NIST and the FBI, was published in Forensic Science International: Genetics.

“If you’re working criminal cases, you need to be able to generate match statistics,” said Katherine Gettings, the NIST biologist who led the study. “The data we’ve published will make it possible for labs that use NGS to generate those statistics.”

For more information about this assembly, please note the NCBI resources:

http://www.ncbi.nlm.nih.gov/genome/51

The Human Cell Atlas (HCA) is a global collaboration to map and characterize all cells in a healthy human body: cell types, numbers, locations, relationships, and molecular components. It will require advances in single-cell RNA sequencing, image-based transcriptomics and proteomics, tissue handling protocols, data analysis, and more. Once complete, it will be a fundamental resource for scientists, allowing them to better understand how healthy cells work, and what goes wrong when disease strikes.

The idea for the HCA grew from an enthusiastic scientific community, and represents a collaborative effort to increase the impact of single-cell biology by federating results from different organs, cell types, experimental approaches, and countries, without suppressing the dynamism of individual communities and projects. The HCA project welcomes participation by scientists, physicians, and engineers around the world. CZI joins groups such as the Wellcome Trust, the European Bioinformatics Institute (EMBL-EBI), the Broad Institute, the Sanger Institute, and UC Santa Cruz to support this work. We are supporting the HCA through a variety of mechanisms, including:

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This directory contains the Sierra Leone 2014 (G3683/KM034562.1/eboVir3)

Assembly of the Ebola virus 2014 genome

(eboVir3, West Africa 01 June 2014 EBOV/G3683/KM034562.1).

For more information about this assembly, please note the NCBI resources:

http://www.ncbi.nlm.nih.gov/genome/4887


The status quo of economies today seems to be leaning towards automation as the base provider of all products and services. Owing to rise of robots in factories and AI in computing, automation is becoming one of the most integral parts of society.

While self-replicating robots have largely been kept to science fiction books, their rise is becoming more and more likely with the rise of supplementary technologies such as 3D printing.

This technology could hold the key to a truly post-scarcity society. The question then arises, how would the rise of a post-scarcity society affect human institutions such as economy and governance that rely on scarcity?

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