An international collaboration of researchers from the RIKEN Center for Biosystems Dynamics Research (BDR) in Japan and Gladstone Institutes in the USA have generated 3D blastocyst-like structures from stem cells.
After many more cell divisions, the embryo turns into a blastocyst that is implanted in the womb where it differentiates and grows into a fetus.
Inside are pluripotent cells--cells that can become any type of cell in the body, but not the placenta--while the outer shell is made from trophoblasts--cells that eventually form the placenta.
For several years, scientists have been able to convert somatic cells--like skin cells--into pluripotent cells.
In an earlier study conducted at Gladstone, authors Cody Kime and Kiichiro Tomoda were able to convert pluripotent mouse cells from an implanted-like state to a pre-implanted state.
As Kime explains, "over seven years ago, our reprogramming experiments suggested that we had found a way to increase cell potency beyond pluripotency, which was unlikely and had not been seen before.
Thin films made of carbon nanotubes hold a lot of promise for advanced optoelectronics, energy and medicine, however with their manufacturing process subject to close supervision and stringent standardization requirements, they are unlikely to become ubiquitous anytime soon.
"A major hindrance to unlocking the vast potential of nanotubes is their multiphase manufacturing process which is extremely difficult to manage.
We have suggested using artificial neural networks (ANN) to analyze experimental data and predict the efficiency of single-walled carbon nanotubes synthesis," explains one of the authors of the study and Skoltech researcher, Dmitry Krasnikov.
In their work published in the prestigious Carbon journal, the authors show that machine learning methods, and, in particular, ANN trained on experimental parameters, such as temperature, gas pressure and flow rate, can help monitor the properties of the carbon nanotube films produced.
"The development of human civilization and the advancement of the materials manufacturing and application technologies are closely interlinked in the era of information and technology, with both materials and computational algorithms and their applications shaping our day-to-day life.
This is equally true for ANN that have evolved into an indispensable tool for dealing with multi-parameter tasks, which run the gamut from object recognition to medical diagnosis.
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