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Puerto Rico's iconic telescope facilitated important scientific discoveries while inspiring young scientists and the public imagination.
- The Arecibo Observatory's main telescope collapsed on Tuesday morning.
- Although officials had been planning to demolish the telescope, the accident marked an unceremonious end to a beloved astronomical tool.
- The Arecibo radio telescope has facilitated many discoveries in astronomy, including the mapping of near-Earth asteroids and the detection of exoplanets.
Bradley Rivera via twitter.com<p>In 1963, the concave dish was built into a natural sinkhole on the northern coast of Puerto Rico. The location was <a href="https://www.space.com/20984-arecibo-observatory.html" target="_blank">picked because it was near the equator,</a> providing scientists a clear view of planets passing overhead, and also of the ionosphere, which is the uniquely reactive layer of Earth's upper atmosphere where the northern lights form.</p><p>Since its construction, scientists have used the Arecibo telescope to map near-Earth asteroids, detect gravitational waves, study pulsars, detect exoplanets and <a href="https://www.seti.org/goodbye-arecibo" target="_blank">search for alien civilizations</a>, among other projects. Here's a brief look at some of the discoveries and accomplishments made using the Arecibo telescope:</p><ul><li>1964: Astronomer <a href="https://en.wikipedia.org/wiki/Gordon_Pettengill" target="_blank" rel="noopener noreferrer">Gordon Pettengill</a> discover that Mercury's rotation period is 59 days, significantly shorter than the previous prediction of 88 days.</li><li>1974: Physicists Russell Alan Hulse and Joseph Hooton Taylor Jr. discover the first first binary pulsar, for which they won a Nobel Prize in Physics.</li><li>1974: Scientists use the telescope to transmit the "Arecibo message" to <a href="https://en.wikipedia.org/wiki/Great_Globular_Cluster_in_Hercules" target="_blank" rel="noopener noreferrer">globular star cluster M13</a>. The message, when translated into image form, contains basic information about humanity and human knowledge: the numbers one to 10, a map of our solar system, an illustration of a human being and the atomic numbers of certain elements.</li><li>1989: Scientists use the telescope to image an asteroid for the first time.</li><li>1992: Astronomers Alex Wolszczan and Dale Frail become the first to discover exoplanets.</li></ul>
The recent AstraZeneca offers a cautionary tale.
- AstraZeneca's press release about its recent vaccine trials was filled with erroneous data.
- A manufacturing error meant that some participants only received half of the intended dosage.
- In the rush to produce a vaccine, science by press release is of growing concern.
AstraZeneca Vaccine Trial Likely Needs a Restart: Johns Hopkins<span style="display:block;position:relative;padding-top:56.25%;" class="rm-shortcode" data-rm-shortcode-id="2913149af137b447f306b83ee2808ced"><iframe type="lazy-iframe" data-runner-src="https://www.youtube.com/embed/YsfvESm84PQ?rel=0" width="100%" height="auto" frameborder="0" scrolling="no" style="position:absolute;top:0;left:0;width:100%;height:100%;"></iframe></span><p>Science has always been and will continue to be complicated. In regards to the pandemic, more surprises await, like the fact that <a href="https://www.theguardian.com/world/2020/nov/17/chemicals-found-in-everyday-products-could-hinder-covid-19-vaccine" target="_blank">PFAS could negatively impact</a> the efficacy of <em>any</em> COVID-19 vaccine. This is of particular importance to Americans, as this acid is used in many common products in this country. <br></p><p>There's little comfort that an adjunct professor at the Harvard School of Public Health says we "have to cross our fingers and hope for the best" in regards to the possibility that the chemicals in non-stick pans and waterproof clothing might thwart our chance of successful vaccination. Discovering this possibility isn't a conspiracy; it's indicative of science working as intended, even if we don't like the results. </p><p>Chemistry matters; so does patience. Weill Cornell Medicine vaccine researcher John Moore <a href="https://www.sciencemag.org/news/2020/11/after-dosing-mix-latest-covid-19-vaccine-success-comes-big-question-mark" target="_blank">phrased it best</a> when calling AstraZeneca's head-scratching announcement "the worst aspect of science by press release." In the rush to deliver good news during a challenging year, we overlook the fact that science is a slow process governed by consensus. Rushing out half-baked data does no one any good. </p><p>AstraZeneca's rush to break news is especially perilous given the <a href="https://www.pewresearch.org/science/2020/09/17/u-s-public-now-divided-over-whether-to-get-covid-19-vaccine/" target="_blank" rel="noopener noreferrer">growing influence</a> of vaccine hesitancy and anti-vaxxers. Misinformation is like a dry forest floor after a hot summer. Vaccine science needs to be evidence-based. Fear-mongering thrives when the focus is a headline instead of clinical efficacy.</p>
Photo: Raquel / Adobe Stock<p>As Jonathan Berman writes in his recent book, <em>Anti-vaxxers: How to Challenge a Misinformed Movement</em>,<br></p><p style="margin-left: 20px;">"Vaccination occupies a unique space as one of the most effective technologies ever developed to fight disease, as well as the only technology to ever eliminate a disease entirely. Vaccination conveys both individual and collective benefits, and carries very modest individual and collective risks." </p><p>Perhaps because this is the first global pandemic in generations we've forgotten how deadly diseases can be. In the 18th century, more humans died from communicable diseases than today's biggest killers, like heart disease and cancer; roughly 300 million people died from smallpox in the 20th century. COVID-19 isn't nearly as deadly, yet that doesn't dampen the real problems we face around vaccine disinformation. </p><p>In some ways, even the botched press release isn't new. Conceptually, vaccines are thousands of years old. Louis Pasteur, building on Edward Jenner's work on cowpox, was as much publicist as scientist when his <a href="https://www.cdc.gov/anthrax/resources/history/index.html" target="_blank">anthrax trial</a> helped usher in the modern age of medicine. He made sure to invite plenty of journalists to observe his trial, which is how word of this medical advancement spread widely. </p><p>Expediency often sacrifices integrity. Fortunately, Pasteur's scientific literacy was as dependable as his love of fawning writers. As Victorian-era statistician Francis Galton presciently commented, "In science credit goes to the man who convinces the world, not the man to whom the idea first occurs." </p><p>We have to wield the power responsibly. Vaccine development by press release does not serve anyone. There are too many variables in medicine and humans are impatient animals. Good science relies on the input of many researchers and tens of thousands of volunteers. </p><p>That great strides have been made in the development of a COVID-19 vaccine should comfort us—a little—but also serve as a reminder that little arrives as quickly as we desire. That's just not how science works. </p><p>--</p><p><em>Stay in touch with Derek on <a href="http://www.twitter.com/derekberes" target="_blank" rel="noopener noreferrer">Twitter</a> and <a href="https://www.facebook.com/DerekBeresdotcom" target="_blank" rel="noopener noreferrer">Facebook</a>. His new book is</em> "<em><a href="https://www.amazon.com/gp/product/B08KRVMP2M?pf_rd_r=MDJW43337675SZ0X00FH&pf_rd_p=edaba0ee-c2fe-4124-9f5d-b31d6b1bfbee" target="_blank" rel="noopener noreferrer">Hero's Dose: The Case For Psychedelics in Ritual and Therapy</a>."</em></p>
The Google-owned company developed a system that can reliably predict the 3D shapes of proteins.
- Scientists have long been puzzled by how specific chains of amino acids go on to form three-dimensional proteins.
- DeepMind developed a system that's able to predict "protein folding" in a fraction of the time of human experiments, and with unprecedented accuracy.
- The achievement could greatly improve drug research and development, as well as bioengineering pursuits.
DeepMind<p>In the biennial competition, teams analyze about 100 proteins with the goal of predicting their eventual 3D shape. A protein's shape determines its function. For example, a protein can become an antibody that binds to foreign particles to protect, an enzyme that carries out chemical reactions, or a structural component that supports cells.</p><p>Proteins start as a string of hundreds of amino acids. Within a protein, pairs of amino acids can interact in numerous ways, and these particular interactions determine the final shape of the protein. But given the sheer number of possible interactions, it's incredibly difficult to predict a protein's physical shape. Difficult, but not impossible.</p><p>Since CASP began, scientists have been able to predict the shape of some simple proteins with reasonable accuracy. CASP is able to verify the accuracy of these predictions by comparing them to the actual shape of proteins, which it obtains through the unpublished results of lab experiments.</p><p>But these experiments are difficult, often taking months or years of hard work. The shapes of some proteins have eluded scientists for decades. As such, it's hard to overstate the value of having an AI that's able to churn out this work in just hours, or even minutes.</p><p>In 2018, DeepMind, which was acquired by Google in 2014, startled the scientific community when its AlphaFold algorithm won the CASP13 contest. AlphaFold was able to predict protein shapes by "training" itself on vast amounts of data on known amino acid strings and their corresponding protein shapes.</p><p>In other words, AlphaFold learned that particular amino acid configurations — say, distances between pairs, angles between chemical bonds — signalled that the protein would likely take a particular shape. AlphaFold then used these insights to predict the shapes of unmapped proteins. AlphaFold's performance in the 2018 contest was impressive, but not reliable enough to consider the problem of "protein folding" solved.</p>
DeepMind<p>In the latest contest, DeepMind used an updated version of AlphaFold. It combines the previous deep-learning strategy with a new "attention algorithm" that accounts for physical and geometric factors. Here's how <a href="https://deepmind.com/blog/article/alphafold-a-solution-to-a-50-year-old-grand-challenge-in-biology" target="_blank">DeepMind describes it:</a></p><p style="margin-left: 20px;">"A folded protein can be thought of as a "spatial graph", where residues are the nodes and edges connect the residues in close proximity. This graph is important for understanding the physical interactions within proteins, as well as their evolutionary history."</p><p style="margin-left: 20px;">"For the latest version of AlphaFold, used at CASP14, we created an attention-based neural network system, trained end-to-end, that attempts to interpret the structure of this graph, while reasoning over the implicit graph that it's building. It uses evolutionarily related sequences, multiple sequence alignment (MSA), and a representation of amino acid residue pairs to refine this graph."</p><p>CASP measures prediction accuracy through the "Global Distance Test (GDT)", which ranges from 0-100. The new version of AlphaFold scored a median of 92.4 GDT for all targets.</p>
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