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Category: biological – Page 234

In part 2 of our plant synthetic biology series we teamed up with Cameron Tout of the Legume Laboratory blog to introduce some of the tools of plant synbio and how these are being applied to agriculture.

Over 9000 years ago the first domesticated varieties of wheat were created in South West Asia. What was remarkable about these plants is that they were selected by humans to retain their seeds rather than dispersing them by wind. This meant that wheat became dependent on farmers for propagation, but allowed people to harvest grain without the pods shattering in their hands.

Since then, humans have been modifying plants in ever more sophisticated ways, the 20th century saw the introduction of mutation breeding and hybrid technology, resulting in massive gains in crop yields.

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A new “nano scalpel” enables scientists at DESY to prepare samples or materials with nanometre precision while following the process with a scanning electron microscope. The Focused Ion Beam, or FIB, microscope which has now gone into service also allows a detailed view of the inner structure of materials. The device was purchased by the University of Bayreuth, as part of a joint research project on the DESY campus funded by the Federal Ministry of Research. The FIB will be operated at the DESY NanoLab jointly with the University of Bayreuth.

“The microscope is not only able to examine microscopic defects, cracks or point-like corrosion sites underneath the surfaces of , but also to machine the surface of samples with extremely high precision, on a nanometre scale,” explains Maxim Bykov, project scientist from the University of Bayreuth. A nanometre is a millionth of a millimetre. The can be used to remove material as though it were a microscopic milling machine; as a result, the combined ion beam and electron microscope is particularly interesting for a wide range of applications in nanotechnology, materials science and biology.

“Apart from examining the structure of materials, the ability of the ion beam to remove material also leads to a wide range of different applications,” says Natalia Dubrovinskaia who is a professor at the University of Bayreuth and in charge of the joint research project (No. 05K13WC3). One example is the preparation of tiny diamond anvils, which are used to hold samples during ultra high-pressure experiments. The diamonds used for this are so small that there is no other way of preparing them. The ion beam allows so-called double-staged diamond anvil cells to be prepared with nanometre precision. The ultra high-pressure experiments are carried out at DESY’s Extreme Conditions Beamline (ECB) P02.2, headed by DESY scientist Hanns-Peter Liermann.

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Nice.

Networks are mathematical representations to explore and understand diverse, complex systems—everything from military logistics and global finance to air traffic, social media, and the biological processes within our bodies. In each of those systems, a hierarchy of recurring, meaningful internal patterns—such as molecules and proteins interacting inside cells, and capacitors and resistors operating within integrated circuits—determines the functions or behaviors of those systems. The larger and more intricate a system is, however, the harder it is for current network modeling techniques to uncover these patterns and represent them in organized, easy-to-understand ways.

Researchers at Stanford University, funded by DARPA’s Simplifying Complexity in Scientific Discovery (SIMPLEX) program, have made progress in overcoming these challenges through a framework they have developed for identifying and clustering what mathematicians call “motifs”: essential but often obscure patterns within systems that are the building blocks of mathematical modeling and that facilitate the computational representation of complex systems.

A research paper describing the team’s achievement was published in Science (“Higher-order organization of complex networks”). At the heart of the team’s success was the creation of algorithms that can automatically explore and prioritize the hidden patterns in data that are fundamental to explaining network structure and function.

a mathematical framework that automatically identifies and prioritizes the patterns that are fundamental to explaining network structure and function

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As we continue to improve cell circuitry, we will see this is going to be more and more important to our tech future. I believe once we have the underlying infrastructure improved with QC that we will see more advancement made in Biocomputing as well as opportunities to adopt on multiple levels including Singularity.

Cells that are electrically active and that also produce light for easy voltage monitoring could lead to new studies of heart arrhythmias and possibly bio-computing.

The human heartbeat is produced by electrical pulses that propagate through cardiac tissue, causing rhythmic muscle contraction. Researchers have previously engineered cells to create an artificial tissue capable of producing coordinated electrical activity, and now a team has added the ability to monitor their electrical state by detecting fluorescent emission. They have also fashioned the cells into “living circuits” that might act as model systems for studying heart behavior.

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It may seem like something from science fiction, but researchers have found a group of microorganisms that can live off of pure electricity, reports. All life uses electricity, but scientists long thought it impossible for a cell to directly consume and expel electrons. That’s because fatty cell membranes act as insulators, preventing the flow of electricity. Scientists have now found evidence that some cells can discharge electrons through specialized proteins in their membranes, and others can ingest electrons from an electrode by using an enzyme that creates hydrogen atoms. Still others might be able to directly consume electrons, though that research has yet to be published. The findings could help researchers understand how life thrives under a variety of conditions, and how it could exist on places like Mars.

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Mobile phones have become commonplace. Modern communication devices like mobile phones need to exchange huge amounts of information. However, what is hidden underneath the elegantly shaped plastic casings is quickly forgotten: Complex signal processors constantly fighting against noise and steadily adapting themselves to changing environment.

But noise and changing environmental conditions do not only affect electrical circuits. In synthetic biology scientists are facing similar problems. However, in synthetic biology a methodology to deal with noise does not exist yet. Prof. Mustafa Khammash and Christoph Zechner of the Department of Biosystems Science and Engineering have studied how conventional signal processors can be translated into biochemical processes — built and operated inside living cells.

A major limitation in engineering biological circuits is that host cells — even if they are genetically identical — are never the same. For instance, cell A might be in a different cell-cycle stage or have more ribosomes available than cell B. Therefore, the same synthetic circuit may behave very differently in each of these two cells. In extreme cases, only a small fraction of cells might show the correct behavior, while the remaining cells act unpredictably. This is referred to as context-dependency.

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There’s a saying among futurists that a human-equivalent artificial intelligence will be our last invention. After that, AIs will be capable of designing virtually anything on their own — including themselves. Here’s how a recursively self-improving AI could transform itself into a superintelligent machine.

When it comes to understanding the potential for artificial intelligence, it’s critical to understand that an AI might eventually be able to modify itself, and that these modifications could allow it to increase its intelligence extremely fast.

Once sophisticated enough, an AI will be able to engage in what’s called “recursive self-improvement.” As an AI becomes smarter and more capable, it will subsequently become better at the task of developing its internal cognitive functions. In turn, these modifications will kickstart a cascading series of improvements, each one making the AI smarter at the task of improving itself. It’s an advantage that we biological humans simply don’t have.

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For robots; the bigger question where is the bigger ROI? Robots trying to be built to out do people; or is it better to enhance people? DARPA is more focused on enhancing people such as soldiers; and I agree with DARPA.

Understanding the hierarchical structure of biological networks like human brain — a network of neurons — could be useful in creating more complex, intelligent computational brains in the fields of artificial intelligence and robotics, says a study.

Like large businesses, many biological networks are hierarchically organised, such as gene, protein, neural, a…

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