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Blog – Page 11812

Behold – the only known example of a biological wheel. Loved by creationists, who falsely think they are examples of “intelligent design”, the bacterial flagellum is a long tail that is spun like a propeller by nano-sized protein motors.

Now these wheels and their gearing have been imaged in high resolution and three dimensions for the first time. Morgan Beeby and his colleagues at Imperial College London used an electron microscope to resolve the mechanisms that provide different amounts of torque to the motors.

The motors are diverse, coming in a wide variety of shapes, sizes and power outputs. Indeed, the diversity of the motors and the fact that they have evolved many times in different bacterial lineages, scuppers the creationist view that the machinery is “irreducibly complex”.

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A team of researchers at The Hong Kong Polytechnic University (PolyU) has designed a biosensor that uses an optical method called upconversion luminescence resonance energy transfer (LRET) for virus detection within 2–3 hours. Its cost is around HK$20 ($2.50) per sample—about 80% lower than traditional testing methods—and can be used for detecting different types of viruses, shedding new light on the development of low-cost, rapid, and ultrasensitive detection of different viruses.

Related: Infectious disease control with portable CMOS-based diagnostics

Traditional biological methods for flu virus detection include genetic analysis—reverse transcription-polymerase chain reaction (RT-PCR) and enzyme-linked immunosorbent assay (ELISA) used in immunology. However, RT-PCR is expensive and time-consuming, while the sensitivity for ELISA is relatively low. Such limitations make them difficult for clinical use as a front-line and onsite diagnostic tool for virus detection.

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Good news for MRIs; maybe witht he precision we also may not have to do any repeat scans as well.

Precision Glass & Optics recently announced the customization of two thin film optical components for a high-field magnetic resonance imaging (MRI) accessory. They developed the dielectric cold mirror and cylindrical prism mirror for the Real Eye Nano; an advanced visual presentation and eye-tracking system constructed of glass and plastic with a reduced size for operation in confined MRI spaces.

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Interesting position that IBM is taking with Quantum Computing. The one challenge that was highlighted in this article around unstable particles actually has been in the process of being resolved by Charles Marcus and colleagues at the University of Copenhagen’s Niels Bohr Institute; Univ. of Copenhagan’s report came out a few weeks ago and it may be a good thing for IBM to connect with the University so they can see how this was resolved.

Also, I don’t believe that we have 3 uniquely different platforms of Quantum as this article highlights. Trying to state that a D-Wave Quantum Computer is not a full Quantum platform or less of a Quantum Platform to is not a fair statement; and I encourage others to pull back from that perspective at this point until Quantum Computing is more evolved and standards around the platform is well defined and approved by industry. Also, the Gartner graph in this article is not one that I embraced given the work on Quantum is showing us the we’re less than 10 yrs away for it in the mainstream instead of Gartners graph showing us Quantum will require more than 10 years to hit the mainstream. And, I saw some of missed marks on Bio-sensors and BMI technology taking more than 10 years on the Gartner graph which is also incorrect since we hearing this week announcements of the new bio-chips which enables bio-sensors and BMIs are making some major steps forward with various devices and implants.

The 3 Types Of Quantum Computers And Their Applications by Jeff Desjardins, Visual Capitalist

It’s an exciting time in computing.

Continue reading “Quantum Computers And Their Applications [INFOGRAPHIC]” | >

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“Dirk Ahlborn, chief executive officer of Hyperloop Transportation Technologies (HTT), announced on Thursday that HTT has reached an agreement with the Slovakian government to explore building a local Hyperloop system. A transport system capable of speeds of up to 760mph (1,223kph).

According to Ahlborn, the next steps will include identifying a route that could connect Bratislava, Slovakia’s capital, with Vienna and Budapest.”

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Mathematicians have discovered a surprising pattern in the expression of prime numbers, revealing a previously unknown “bias” to researchers.

Primes, as you’ll hopefully remember from fourth-grade math class, are numbers that can only be divided by one or themselves (e.g. 2, 3, 5, 7, 11, 13, 17, etc.). Their appearance in the roll call of all integers cannot be predicted, and no magical formula exists to know when a prime number will choose to suddenly make an appearance. It’s an open question as to whether or not a pattern even exists, or whether or not mathematicians will ever crack the code of primes, but most mathematicians agree that there’s a certain randomness to the distribution of prime numbers that appear back-to-back.

Or at least that’s what they thought. Recently, a pair of mathematicians decided to test this “randomness” assumption, and to their shock, they discovered that it doesn’t actually exist. As reported in New Scientist, researchers Kannan Soundararajan and Robert Lemke Oliver of Stanford University in California have detected unexpected biases in the distribution of consecutive primes.

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Or not.

It was hailed as the most significant test of machine intelligence since Deep Blue defeated Garry Kasparov in chess nearly 20 years ago. Google’s AlphaGo has won two of the first three games against grandmaster Lee Sedol in a Go tournament, showing the dramatic extent to which AI has improved over the years. That fateful day when machines finally become smarter than humans has never appeared closer—yet we seem no closer in grasping the implications of this epochal event.

Indeed, we’re clinging to some serious—and even dangerous—misconceptions about artificial intelligence. Late last year, SpaceX co-founder Elon Musk warned that AI could take over the world, sparking a flurry of commentary both in condemnation and support. For such a monumental future event, there’s a startling amount of disagreement about whether or not it’ll even happen, or what form it will take. This is particularly troubling when we consider the tremendous benefits to be had from AI, and the possible risks. Unlike any other human invention, AI has the potential to reshape humanity, but it could also destroy us.

It’s hard to know what to believe. But thanks to the pioneering work of computational scientists, neuroscientists, and AI theorists, a clearer picture is starting to emerge. Here are the most common misconceptions and myths about AI.

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