Phil and Stephen review more reasons that this is a great time to be alive. The little lizard with the big genome. Making skins cells into stem cells — three different ways! The check-in procedure that could save your life. PLUS Geekout: deciphering the Voynich manuscript via AI.
Scientists have decoded the genome of the axolotl, the Mexican amphibian with a Mona Lisa smile. It has 32 billion base pairs, which makes it ten times the size of the human genome, and the largest genome ever sequenced.
The axolotl, endangered in the wild, has been bred in laboratories and studied for more than 150 years. It has the remarkable capacity to regrow amputated limbs complete with bones, muscles and nerves; to heal wounds without producing scar tissue; and even to regenerate damaged internal organs.
This is the first salamander genome ever sequenced. The reason it took so long is that it has so many repetitive parts, according to Elly M. Tanaka, a senior scientist at the Research Institute of Molecular Pathology in Vienna and senior author of the new study.
The researchers have identified some of the genes involved in regeneration, and some genes that exist only in the axolotl, but there is much work still to be done.
If you lose an arm or a leg, there’s a whole range of advanced prosthetics to give you some functionality back. But we might not need any artificial help in the long run if research into limb regeneration bears fruit. Scientists have now sequenced the genome of the Mexican axolotl, and have identified a few key genes hidden amongst its extremely complex genetic blueprint.
In a scientific first, researchers at the Gladstone Institutes turned skin cells from mice into stem cells by activating a specific gene in the cells using CRISPR technology. The innovative approach offers a potentially simpler technique to produce the valuable cell type and provides important insights into the cellular reprogramming process.
In 2006, Gladstone Senior Investigator Shinya Yamanaka, MD, PhD, discovered he could make stem cells—dubbed induced pluripotent stem cells (iPSCs)—by treating ordinary skin cells with four key proteins. These proteins, called transcription factors, work by changing which genes are expressed in the cell, turning off genes associated with skin cells and turning on genes associated with stem cells.
Building on this work, Ding and others previously created iPSCs not with transcription factors, but by adding a cocktail of chemicals to the cells. The latest study, published in Cell Stem Cell, offers a third way to turn skin cells into stem cells by directly manipulating the cells’ genome using CRISPR gene regulation techniques.
Some of Google’s top AI researchers are trying to predict your medical outcome as soon as you’re admitted to the hospital.
A new research paper, published Jan. 24 with 34 co-authors and not peer-reviewed, claims better accuracy than existing software at predicting outcomes like whether a patient will die in the hospital, be discharged and readmitted, and their final diagnosis. To conduct the study, Google obtained de-identified data of 216,221 adults, with more than 46 billion data points between them. The data span 11 combined years at two hospitals, University of California San Francisco Medical Center (from 2012-2016) and University of Chicago Medicine (2009-2016).
While the results have not been independently validated, Google claims vast improvements over traditional models used today for predicting medical outcomes. Its biggest claim is the ability to predict patient deaths 24-48 hours before current methods, which could allow time for doctors to administer life-saving procedures.
The first step was to figure out the language of the ciphered text. To that end, an AI studied the text of the “Universal Declaration of Human Rights” as it was written in 380 different languages, looking for patterns. Following this training, the AI analyzed the Voynich gibberish, concluding with a high rate of certainty that the text was written in encoded Hebrew. Kondrak and Hauer were taken aback, as they went into the project thinking it was formed from Arabic.
For the second step, the researchers entertained a hypothesis proposed by previous researchers—that the script was created with alphagrams, that is, words in which text has been replaced by an alphabetically ordered anagram (For example, an alphagram of GIZMODO would read DGIMOOZ). Armed with the knowledge that text was originally coded from Hebrew, the researchers devised an algorithm that could take these anagrams and create real Hebrew words.
First sentence in the document (with a little additional help from Google translate to get the word order for English): “She made recommendations to the priest, man of the house and me and people,’” It’s a really weird way to open up a 240-page manuscript, but the phrase actually makes some sense. Importantly, the researchers aren’t saying they’ve deciphered the entire Voynich manuscript. Rather, they’ve identified the language of origin (Hebrew), and a coding scheme in which letters have been arranged in a particular order (alphagram). Kondrak says the full meaning of the text won’t be known until historians of ancient Hebrew have a chance to study the deciphered text.
Eternity Kevin MacLeod (incompetech.com) | Licensed under Creative Commons: By Attribution 3.0 License | http://creativecommons.org/licenses/by/3.0/