Though it may be hard to believe that there is already an “established” method of doing something such as 3D-printing biological tissue, there does indeed seem to be one. It utilizes microscale scaffolds – which a newly-developed technique does away with the need for.
“Previously, bacteria were found using metagenomics or microbiome sequencing, and now we have confirmed that signal based on our ability to label the bacterial RNA with a florescent ‘tag’ and actually see them,” said Dr. Maxim Seferovic, instructor in obstetrics and gynecology at Baylor and lead author in the study. “We leveraged a powerful new imaging technology to add greater specificity in the signal of bacterial RNA, which helped us to see bacteria within the microarchitecture of the placental tissue.”
Researchers examined microbes in term and preterm gestations using a signal amplified 16S universal in situ hybridization probe designed for bacterial rRNA, along with several other histologic methods. Seferovic said the study was carefully designed to control for contamination as best as possible, so that these sparse bacteria could be accurately attributed to their location in the placenta.
“We did not see quantitative or numerical differences between preterm or full-term births, nor did we see them localizing to different substrata. But we do see differences in what genera of bacteria are there in preterm or full term, and this supported our and other’s past findings as well,” said Aagaard.
Tam Hunt interviews Prof. Morgan Levine about her work with epigenetics and aging.
One of the biggest breakthroughs in biology in the last few decades has been the discovery of epigenetics. Rather than changing the genes themselves, epigenetics change how genes are expressed, allowing our cells to differentiate between their various types.
However, the epigenetics of our cells change over time. There is some debate over how much epigenetic alterations are a cause or a consequence of other age-related damage, but they are one of the primary hallmarks of aging.
Kyle Reese: The Terminator’s an infiltration unit, part man, part machine. Underneath, it’s a hyperalloy combat chassis — micro processor-controlled, fully armored. Very tough. But outside, it’s living human tissue — flesh, skin, hair, blood, grown for the cyborgs…
3D bioprinting is the automated fabrication of multicellular tissue via spatially defined deposition of cells. The ability to spatially control deposition in the x, y and z axes allows for creation of tissue-specific patterns or compartments, with in vivo-like architecture that mimics key aspects of native biology.
3D bioprinted tissues exhibit a microenvironment more suited to in vivo-like cellular function in comparison to traditional 2D monoculture (or monolayer co-cultures), as well as maintenance of a more defined architecture than is observed in self-aggregated co-culture models.
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Research by neuroscientists at the University of Chicago shows how short-term, working memory uses networks of neurons differently depending on the complexity of the task at hand.
The researchers used modern artificial intelligence (AI) techniques to train computational neural networks to solve a range of complex behavioral tasks that required storing information in short term memory. The AI networks were based on the biological structure of the brain and revealed two distinct processes involved in short-term memory. One, a “silent” process where the brain stores short-term memories without ongoing neural activity, and a second, more active process where circuits of neurons fire continuously.
The study, led by Nicholas Masse, Ph.D., a senior scientist at UChicago, and senior author David Freedman, Ph.D., professor of neurobiology, was published this week in Nature Neuroscience.
Continue reading “AI software reveals the inner workings of short-term memory” »
A group of researchers at Sandia National Laboratories have developed a tool that can cross-train standard convolutional neural networks (CNN) to a spiking neural model that can be used on neuromorphic processors. The researchers claim that the conversion will enable deep learning applications to take advantage of the much better energy efficiency of neuromorphic hardware, which are designed to mimic the way the biological neurons work.
The tool, known as Whetstone, works by adjusting artificial neuron behavior during the training phase to only activate when it reaches an appropriate threshold. As a result, neuron activation become a binary choice – either it spikes or it doesn’t. By doing so, Whetstone converts an artificial neural network into a spiking neural network. The tool does this by using an incremental “sharpening process” (hence Whetstone) through each network layer until the activation becomes discrete.
According to Whetstone researcher Brad Aimone, this discrete activation greatly minimizes communication costs between the layers, and thus energy consumption, but with only minimal loss of accuracy. “We continue to be impressed that without dramatically changing what the networks look like, we can get very close to a standard neural net [in accuracy],” he says. “We’re usually within a percent or so on performance.”
Continue reading “One Step Closer to Deep Learning on Neuromorphic Hardware” »
As living organisms eat, grow, and self-regenerate, all the while they are slowly dying. Chemically speaking, this is because life is thermodynamically unstable, while its ultimate waste products are in a state of thermal equilibrium. It’s somewhat of a morbid thought, but it’s also one of the characteristics that is common to all forms of life.
Now in a new study, researchers have created a self-replicator that self-assembles while simultaneously being destroyed. The synthetic system may help researchers better understand what separates biological matter from simpler chemical matter, and also how to create synthetic life in the lab.
The researchers, Ignacio Colomer, Sarah Morrow, and Stephen P. Fletcher, at the University of Oxford, have published a paper on the self-replicator in a recent issue of Nature Communications.
