Tag Archives: machine

Cells Are Like Robust Computational Systems, Scientists Report

Gene regulatory networks in cell nuclei are similar to cloud computing networks, such as Google or Yahoo!, researchers report today in the online journal Molecular Systems Biology. The similarity is that each system keeps working despite the failure of individual components, whether they are master genes or computer processors.

This finding by an international team led by Carnegie Mellon University computational biologist Ziv Bar-Joseph helps explain not only the robustness of cells, but also some seemingly incongruent experimental results that have puzzled biologists.

“Similarities in the sequences of certain master genes allow them to back up each other to a degree we hadn’t appreciated,” said Bar-Joseph, an assistant professor of computer science and machine learning and a member of Carnegie Mellon’s Ray and Stephanie Lane Center for Computational Biology.

Between 5 and 10 percent of the genes in all living species are master genes that produce proteins called transcription factors that turn all other genes on or off. Many diseases are associated with mutations in one or several of these transcription factors. However, as the new study shows, if one of these genes is lost, other “parallel” master genes with similar sequences, called paralogs, often can replace it by turning on the same set of genes.

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Scientists invent 1.2nm molecular gear

Scientists from A*STAR’s Institute of Materials Research and Engineering (IMRE), led by Professor Christian Joachim, have scored a breakthrough in nanotechnology by becoming the first in the world to invent a molecular gear of the size of 1.2nm whose rotation can be deliberately controlled. This achievement marks a radical shift in the scientific progress of molecular machines and is published in Nature Materials, one of the most prestigious journals in materials science.

Said Prof Joachim, “Making a gear the size of a few atoms is one thing, but being able to deliberately control its motions and actions is something else altogether. What we’ve done at IMRE is to create a truly complete working gear that will be the fundamental piece in creating more complex molecular machines that are no bigger than a grain of sand.”

Prof Joachim and his team discovered that the way to successfully control the rotation of a single-molecule gear is via the optimization of molecular design, molecular manipulation and surface atomic chemistry. This was a breakthrough because before the team’s discovery, motions of molecular rotors and gears were random and typically consisted of a mix of rotation and lateral displacement. The scientists at IMRE solved this scientific conundrum by proving that the rotation of the molecule-gear could be wellcontrolled by manipulating the electrical connection between the molecule and the tip of a Scanning Tunnelling Microscope while it was pinned on an atom axis.

Building The Exascale Computer

What if we gave scientists machines that dwarf today’s most powerful supercomputers? What could they tell us about the nature of, say, a nuclear explosion? Indeed, what else could they discover about the world? This is the story of the quest for an exascale computer – and how it might change our lives.

What is exascale?

One exaflop is 1,000 times faster than a petaflop. The fastest computer in the world is currently the IBM-based Roadrunner, which is located in Los Alamos, New Mexico. Roadrunner runs at an astounding one petaflop, which equates to more than 1,000 trillion operations per second. The supercomputer has 129,600 processing cores and takes up more room than a small house, yet it’s still not quite fast enough to run some of the most intense global weather simulations, nuclear tests and brain modelling tasks that modern science demands. For example, the lab currently uses the processing power of Roadrunner to run complex visual cortex and cellular modelling experiments in almost real- time. In the next six months, the computer will be used for nuclear simulation and stockpile tests to make sure that the US nuclear weapon reserves are safe. However, when exascale calculations become a reality in the future, the lab could step up to running tests on ocean and atmosphere interactions. These are not currently possible because the data streams involved are simply too large. The move to exascale is therefore critical, because researchers require increasingly fast results from their experiments.

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Biodegradable synthetic resin replaces vital body parts

Researchers at the University of Twente (UT) have developed a new type of resin that can be broken down by the body. This new resin makes it possible to replicate important body parts exactly and make them fit precisely.

The resin can be given different properties depending on where in the body it is to be used. Cells can be sown and cultured on these models, so that the tissues grown are, in fact, produced by the body itself. The new resin has been developed by Ferry Melchels and Prof. Dirk Grijpma of the UT’s Polymer Chemistry and Biomaterials research group. An article on this breakthrough will be appearing in the authoritative specialist journal, Biomaterials.

Stereolithography is a technology with which three-dimensional objects can be made from a digital design. It is also possible to scan an object using a CT scanner (or micro-CT scanner) to obtain a digital image. The object in question can subsequently be copied extremely accurately with a stereolithograph. A stereolithograph is therefore a 3D replicating machine with a very high resolution. The way it works is based on the local hardening of a liquid resin with computer-driven light. The resins available for stereolithography so far harden into chemical networks that cannot be broken down.

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Scientists Discover a Hidden “Memory” Switch in Our Brains

If you’ve e ver been educated, and the fact that you’re reading this means that you either have or are extremely good at guessing, you’ve tried to find a way to enhance your memory. Reading things ten times, flash cards, enough coffee to accelerate an elephant to eighty-eight miles an hour – and none of them work. Now scientists may have found an all-purpose “memory on” switch hiding in your head.

A team of German an UK researchers have applied magnetoencephalographic techniques to look inside the very living brain of dozens of people, and if that fact doesn’t impress you chalk one up to “humans can get used to anything.” These people have machines that can scan your mind and draw maps! Sure, those maps are like urban planners trying to document a computer chip, not really sure of which does what or how to represent it, but we can still see some general functions from all the data acquired.

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Building a Brain on a Silicon Chip

An international team of scientists in Europe has created a silicon chip designed to function like a human brain. With 200,000 neurons linked up by 50 million synaptic connections, the chip is able to mimic the brain’s ability to learn more closely than any other machine.

Although the chip has a fraction of the number of neurons or connections found in a brain, its design allows it to be scaled up, says Karlheinz Meier, a physicist at Heidelberg University, in Germany, who has coordinated the Fast Analog Computing with Emergent Transient States project, or FACETS.

The hope is that recreating the structure of the brain in computer form may help to further our understanding of how to develop massively parallel, powerful new computers, says Meier.

This is not the first time someone has tried to recreate the workings of the brain. One effort called the Blue Brain project, run by Henry Markram at the Ecole Polytechnique Fédérale de Lausanne, in Switzerland, has been using vast databases of biological data recorded by neurologists to create a hugely complex and realistic simulation of the brain on an IBM supercomputer.

FACETS has been tapping into the same databases. “But rather than simulating neurons,” says Karlheinz, “we are building them.” Using a standard eight-inch silicon wafer, the researchers recreate the neurons and synapses as circuits of transistors and capacitors, designed to produce the same sort of electrical activity as their biological counterparts.

A neuron circuit typically consists of about 100 components, while a synapse requires only about 20. However, because there are so much more of them, the synapses take up most of the space on the wafer, says Karlheinz.

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The Future of Machine Intelligence

In early March 2009, 100 intellectual adventurers journeyed from various corners of Europe, Asia, America and Australasia to the Crowne Plaza Hotel in Arlington Virginia, to take part in the Second Conference on Artificial General Intelligence, AGI-09: a conference aimed explicitly at the grand goal of the AI field, the creation of thinking machines with general intelligence at the human level and ultimately beyond.

While the majority of the crowd hailed from academic institutions, major firms like Google, GE, AT&T and Autodesk were also represented, along with a substantial contingent of entrepreneurs involved with AI startups, and independent researchers. The conference benefited from sponsorship by several organizations, including KurzweilAI.net, Japanese entrepreneur and investor Joi Ito’s Joi Labs, Itamar Arel’s Machine Intelligence Lab at the University of Tennessee, the University of Memphis, Novamente LLC, Rick Schwall, and the Enhanced Education Foundation.

Since I was the chair of the conference and played a large role in its organization – along with a number of extremely competent and passionate colleagues – my opinion must be considered rather subjective … but, be that as it may, my strong feeling is that the conference was an unqualified success! Admittedly, none of the research papers were written and presented by an AI program, which is evidence that the field still has a long way to go to meet its goals. Still, a great number of fascinating ideas and mathematical and experimental results were reported, building confidence in the research community that real progress toward advanced AGI is occurring.

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Personal Supercomputer Is Coming

Within the next three to four years, most PC users will see their machines morph into personal supercomputers. This change will be enabled by the emergence of multicore CPUs and, perhaps more importantly, the arrival of massively parallel cores in the graphical processing units.

In fact, ATI (a division of Advanced Micro Devices) and Nvidia are already offering multiple programmable cores in their high-end discreet graphics processing platforms. These cores can be programmed to do many parallel processing tasks, resulting in dramatically better display features and functions for video, especially for gaming. But these platforms currently come at a hefty price and often require significant amounts of power, making them impractical in many laptop designs.

But preliminary steps are being taken to make these high-end multicore and programmable components available to virtually any machine. Vendors are moving to create integrated multicore platforms, with 64 or more specialty cores that can be used in conjunction with the various multicore CPUs now taking hold in the market. Using the most advanced semiconductor processes and geometries (32nm and soon 22nm and beyond), these new classes of devices will achieve incredible processing capability. They will also morph from the primarily graphics-oriented tasks they currently perform to include many more tasks associated with business and personal productivity.

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Scientists make quantum leap in developing faster computers

The researchers have created components that could one day be used to develop quantum computers – devices based on molecular scale technology instead of and which would be much faster than conventional computers.

The study, by scientists at the Universities of Manchester and Edinburgh and published in the journal Nature, was funded by the European Commission.

Scientists have achieved the breakthrough by combining with molecular machines that can shuttle between two locations without the use of external force. These manoeuvrable magnets could one day be used as the basic component in quantum computers.

Conventional computers work by storing information in the form of bits, which can represent information in binary code – either as zero or one.

Quantum computers will use quantum , or , which are far more sophisticated – they are capable of representing not only zero and one, but a range of values simultaneously. Their complexity will enable quantum computers to perform intricate calculations much more quickly than conventional computers.

Professor David Leigh, of the University of Edinburgh’s School of Chemistry, said: “This development brings super-fast, non-silicon based computing a step closer.

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On the Quest for Synthetic Life, Scientists Build Their Own Cellular Protein Factory

In an important step towards creating synthetic life forms, genetics pioneer George Church has produced a man-made version of the part of the cell that turns out proteins, which carry out the business of life. “If you going to make synthetic life that is anything like current life … you have got to have this … biological machine,” Church told reporters in a telephone briefing. And it can have important industrial uses, especially for manufacturing drugs and proteins not found in nature [Reuters].

Church’s team built a functional ribosome from scratch, molecule by molecule. Ribosomes are molecular machines that read strands of RNA and translate the genetic code into proteins. They are exquisitely complex, and previous attempts to reconstitute a ribosome from its constituent parts – dozens of proteins along with several molecules of RNA – yielded poorly functional ribosomes, and even then succeeded only when researchers resorted to “strange conditions” that did not recapitulate the environment of a living cell, Church said [Nature blog]. Next, the researchers want to produce man-made ribosomes that can replicate themselves.

Church’s work hasn’t yet been published in a peer-reviewed journal; instead he presented his preliminary results at a seminar of Harvard alumni over the weekend. He described how his research team first disassembled ribosomes from E. coli, a common lab bacterium, into its component molecules. They then used enzymes to put the various RNA and protein components back together. When put together in a test tube, these components spontaneously formed into functional ribosomes…. The researchers used the artificial ribosome to successfully produce the luciferase enzyme, a firefly protein that generates the bug’s glow [Technology Review].

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