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See Jeff Bezos In Space Before A ‘Buck Moon’ Meets Giant Planets: What You Can See In The Night Sky This Week – Forbes



Each Monday I pick out the northern hemisphere’s celestial highlights (mid-northern latitudes) for the week ahead, but be sure to check my main feed for more in-depth articles on stargazing, astronomy, eclipses and more. 

What To Watch For In The Night Sky This Week: July 19-25, 2021

It’s the 52-year anniversary of America’s Apollo 11 Moon landing this week, but expect it to be all about a billionaire in space as Amazon founder Jeff Bezos gets blasted off to space on top of his company Blue Origin’s New Shepard rocket—named after Alan Shepherd’s historic “first American in space” mission in 1961.

After that excitement will come a nice conjunction of Venus and bright star Regulus, the rising of a full “Buck Moon” and the chance to see both Saturn and then Jupiter tangle with our natural satellite. 

Tuesday, July 20, 2021: Blue Origin’s New Shepard launches Jeff Bezos to space

In an event that will last a mere 11 minutes, billionaire founder of Amazon, Jeff Bezos—along with three others—will be launched in a space capsule from West Texas to the fabled Kármán line (62 miles/100km up) that divides Earth from Space. After a few minutes in space it will then float back down to Earth by parachute while the rocket booster lands back on the launch pad.

Happening on the 52-year anniversary of America’s Apollo 11 Moon landing, it will be live-streamed on and YouTube

Wednesday, July 21, 2021: Venus and Regulus in conjunction

The brightest planet in the night sky by far will appear to be just 1° from the 21st brightest star in the sky, Regulus in Leo, in the western sky just after sunset.

About 79 light years from the Sun, Regulus is actually two pairs of stars that orbit each other. Most star systems are not like ours! 

Look to the lower-right of the two and you may see the red planet Mars closer to the horizon.

Friday, July 23, 2021: A Full ‘Buck Moon’ rising

The Moon will be officially full at 02:37 Universal Time on Saturday, July 24, 2021, which in North America means today.

The rising of the full Moon will be best seen in the northeastern direction about 20:30 local time across the U.S. though do check the exact moonrise times for your location. In practice it takes about an additional 10 minutes to actually see the full Moon peeking above the horizon—but what a sight!

Saturday, July 24, 2021: Saturn and a waning ‘Buck Moon’ rising

Although the 98%-lit waning Moon will rise in darkness tonight in the southeastern night sky, it will do so with the ringed planet Saturn about 4° above it.

Binoculars will be handy for glimpsing it (though a telescope is required to see its rings) and also for getting a nice close-up of the Moon’s surface.

Wait long enough and the much brighter planet Jupiter will appear due east, to the Moon’s lower-left. 

Sunday, July 25, 2021: Jupiter and a waning ‘Buck Moon’ rising

Now 94% illuminated, the waning “Buck Moon” will be about 4º below Jupiter a few hours after dark, with dimmer Saturn to its upper right in the southeastern sky.  

Constellation of the week: Hercules

One of the biggest constellations in the night sky and a sure sign that summer has arrived, Hercules is a vast collection of fairly dim stars between two bright summer stars—Vega in Lyra and Arcturus in Boötes.

It’s a vaguely figure-like constellation, but the most easily identifiable part of the square is its “Keystone,” which just happens to be home to its most wondrous deep sky object … 

Object of the week: The Great Globular Cluster in Hercules (M13) 

Is this the most beautiful sight in the entire northern sky? Just about discernible to the naked eye under very dark skies, the Great Globular Cluster in Hercules is astonishing in any binoculars or a small telescope. The closest and the brightest globular cluster to us in the northern hemisphere, it’s about 25,000 light years distant. 

A halo of tightly packed stars that looks rather like a galaxy, a globular cluster is a star cluster that formed outside of our galaxy and now orbits in its outskirts. The 150 we know about are the oldest things you can see in the night sky—and M13 is the brightest and best one we can see from the northern hemisphere.

Times and dates given apply to mid-northern latitudes. For the most accurate location-specific information consult online planetariums like Stellarium and The Sky Live. Check planet-rise/planet-setsunrise/sunset and moonrise/moonset times for where you are.

Wishing you clear skies and wide eyes.

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New 3D images of shark intestines show they work like Tesla valves – CTV News



Despite sharks being frequently cast as the ‘scary creature with large teeth’ in our collective imagination, not a lot is known about what sharks actually eat and how they can go so long between meals.

But new 3D imaging may have unravelled some of those digestive mysteries by creating a better picture of what a shark’s intestines look like.

According to a new study published in the journal Proceedings of the Royal Society B, researchers have made images using CT scans that allow a closer look at the animal’s spiral intestines, which may allow it to digest food slowly.

“It’s high time that some modern technology was used to look at these really amazing spiral intestines of sharks,” Samantha Leigh, assistant professor at California State University and lead author of the study, said in a press release. “We developed a new method to digitally scan these tissues and now can look at the soft tissues in such great detail without having to slice into them.”

Researchers took CT scans of around three dozen shark species from specimens preserved at the Natural History Museum of Los Angeles. This process involved taking a series of X-rays from different angles and then combining those flat images to produce a 3D one.

This meant researchers didn’t have to dissect a shark and disturb the organs involved.

“Intestines are so complex, with so many overlapping layers, that dissection destroys the context and connectivity of the tissue,” co-author Adam Summers, a professor based at UW Friday Harbor Labs, explained in the release. 

“It would be like trying to understand what was reported in a newspaper by taking scissors to a rolled-up copy. The story just won’t hang together.”

By examining these 3D images, researchers were able to theorize that the spiral shape of the sharks intestines actually help them retain food for longer. The spiral shape of the gut slows down the progress of food through the shark, moving based on gravity and the contraction of the intestines.

The release explained that the sharks’ intestines function similarly to a one-way valve designed by Nikola Tesla more than 100 years ago, in that it allows fluid to move in one direction without any backflow or external help from other moving parts.

Contrary to how often the shark in Jaws was seen chowing down, sharks often go for days or even weeks between meals, so these spiral intestines may help them stretch out one large meal, researchers say.

The next step for researchers is to create these structures themselves using a 3D printer, and see what happens when material passes through them in real time. The release also mentioned that these structures could serve as inspiration for technology and things such as wastewater treatment or filtering out microplastics from water.

As sharks eat a wide variety of things in the ocean and are frequently top predators, understanding more about how they digest could help us understand more about the ocean ecosystem in general.

“The vast majority of shark species, and the majority of their physiology, are completely unknown,” Summers said, adding that new things are discovered every time they look closely.

“We need to look harder at sharks and, in particular, we need to look harder at parts other than the jaws, and the species that don’t interact with people.”

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DeepMind puts the entire human proteome online, as folded by AlphaFold – Yahoo Movies Canada



DeepMind and several research partners have released a database containing the 3D structures of nearly every protein in the human body, as computationally determined by the breakthrough protein folding system demonstrated last year, AlphaFold. The freely available database represents an enormous advance and convenience for scientists across hundreds of disciplines and domains, and may very well form the foundation of a new phase in biology and medicine.

The AlphaFold Protein Structure Database is a collaboration between DeepMind, the European Bioinformatics Institute and others, and consists of hundreds of thousands of protein sequences with their structures predicted by AlphaFold — and the plan is to add millions more to create a “protein almanac of the world.”

“We believe that this work represents the most significant contribution AI has made to advancing the state of scientific knowledge to date, and is a great example of the kind of benefits AI can bring to society,” said DeepMind founder and CEO Demis Hassabis.

From genome to proteome

If you’re not familiar with proteomics in general — and it’s quite natural if that’s the case — the best way to think about this is perhaps in terms of another major effort: that of sequencing the human genome. As you may recall from the late ’90s and early ’00s, this was a huge endeavor undertaken by a large group of scientists and organizations across the globe and over many years. The genome, finished at last, has been instrumental to the diagnosis and understanding of countless conditions, and in the development of drugs and treatments for them.

It was, however, just the beginning of the work in that field — like finishing all the edge pieces of a giant puzzle. And one of the next big projects everyone turned their eyes toward in those years was understanding the human proteome — which is to say all the proteins used by the human body and encoded into the genome.

The problem with the proteome is that it’s much, much more complex. Proteins, like DNA, are sequences of known molecules; in DNA these are the handful of familiar bases (adenine, guanine, etc.), but in proteins they are the 20 amino acids (each of which is coded by multiple bases in genes). This in itself creates a great deal more complexity, but it’s only the start. The sequences aren’t simply “code” but actually twist and fold into tiny molecular origami machines that accomplish all kinds of tasks within our body. It’s like going from binary code to a complex language that manifests objects in the real world.

Practically speaking this means that the proteome is made up of not just 20,000 sequences of hundreds of acids each, but that each one of those sequences has a physical structure and function. And one of the hardest parts of understanding them is figuring out what shape is made from a given sequence. This is generally done experimentally using something like x-ray crystallography, a long, complex process that may take months or longer to figure out a single protein — if you happen to have the best labs and techniques at your disposal. The structure can also be predicted computationally, though the process has never been good enough to actually rely on — until AlphaFold came along.

Taking a discipline by surprise

Without going into the whole history of computational proteomics (as much as I’d like to), we essentially went from distributed brute-force tactics 15 years ago — remember Folding@home? — to more honed processes in the last decade. Then AI-based approaches came on the scene, making a splash in 2019 when DeepMind’s AlphaFold leapfrogged every other system in the world — then made another jump in 2020, achieving accuracy levels high enough and reliable enough that it prompted some experts to declare the problem of turning an arbitrary sequence into a 3D structure solved.

I’m only compressing this long history into one paragraph because it was extensively covered at the time, but it’s hard to overstate how sudden and complete this advance was. This was a problem that stumped the best minds in the world for decades, and it went from “we maybe have an approach that kind of works, but extremely slowly and at great cost” to “accurate, reliable, and can be done with off the shelf computers” in the space of a year.

Examples of protein structures predicted by AlphaFold

Examples of protein structures predicted by AlphaFold

Image Credits: DeepMind

The specifics of DeepMind’s advances and how it achieved them I will leave to specialists in the fields of computational biology and proteomics, who will no doubt be picking apart and iterating on this work over the coming months and years. It’s the practical results that concern us today, as the company employed its time since the publication of AlphaFold 2 (the version shown in 2020) not just tweaking the model, but running it… on every single protein sequence they could get their hands on.

The result is that 98.5% of the human proteome is now “folded,” as they say, meaning there is a predicted structure that the AI model is confident enough (and importantly, we are confident enough in its confidence) represents the real thing. Oh, and they also folded the proteome for 20 other organisms, like yeast and E. coli, amounting to about 350,000 protein structures total. It’s by far — by orders of magnitude — the largest and best collection of this absolutely crucial information.

All that will be made available as a freely browsable database that any researcher can simply plug a sequence or protein name into and immediately be provided the 3D structure. The details of the process and database can be found in a paper published today in the journal Nature.

“The database as you’ll see it tomorrow, it’s a search bar, it’s almost like Google search for protein structures,” said Hassabis in an interview with TechCrunch. “You can view it in the 3D visualizer, zoom around it, interrogate the genetic sequence… and the nice thing about doing it with EMBL-EBI is it’s linked to all their other databases. So you can immediately go and see related genes, And it’s linked to all these other databases, you can see related genes, related in other organisms, other proteins that have related functions, and so on.”


“As a scientist myself, who works on an almost unfathomable protein,” said EMBL-EBI’s Edith Heard (she didn’t specify which protein), “it’s really exciting to know that you can find out what the business end of a protein is now, in such a short time — it would have taken years. So being able to access the structure and say ‘aha, this is the business end,’ you can then focus on trying to work out what that business end does. And I think this is accelerating science by steps of years, a bit like being able to sequence genomes did decades ago.”

So new is the very idea of being able to do this that Hassabis said he fully expects the entire field to change — and change the database along with it.

“Structural biologists are not yet used to the idea that they can just look up anything in a matter of seconds, rather than take years to experimentally determine these things,” he said. “And I think that should lead to whole new types of approaches to questions that can be asked and experiments that can be done. Once we start getting wind of that, we may start building other tools that cater to this sort of serendipity: What if I want to look at 10,000 proteins related in a particular way? There isn’t really a normal way of doing that, because that isn’t really a normal question anyone would ask currently. So I imagine we’ll have to start producing new tools, and there’ll be demand for that once we start seeing how people interact with this.”

That includes derivative and incrementally improved versions of the software itself, which has been released in open source along with a great deal of development history. Already we have seen an independently developed system, RoseTTAFold, from researchers at the University of Washington’s Baker Lab, which extrapolated from AlphaFold’s performance last year to create something similar yet more efficient — though DeepMind seems to have taken the lead again with its latest version. But the point was made that the secret sauce is out there for all to use.

Practical magic

Although the prospect of structural bioinformaticians attaining their fondest dreams is heartwarming, it is important to note that there are in fact immediate and real benefits to the work DeepMind and EMBL-EBI have done. It is perhaps easiest to see in their partnership with the Drugs for Neglected Diseases Institute.

The DNDI focuses, as you might guess, on diseases that are rare enough that they don’t warrant the kind of attention and investment from major pharmaceutical companies and medical research outfits that would potentially result in discovering a treatment.

“This is a very practical problem in clinical genetics, where you have a suspected series of mutations, of changes in an affected child, and you want to try and work out which one is likely to be the reason why our child has got a particular genetic disease. And having widespread structural information, I am almost certain will improve the way we can do that,” said DNDI’s Ewan Birney in a press call ahead of the release.

Ordinarily examining the proteins suspected of being at the root of a given problem would be expensive and time-consuming, and for diseases that affect relatively few people, money and time are in short supply when they can be applied to more common problems like cancers or dementia-related diseases. But being able to simply call up the structures of 10 healthy proteins and 10 mutated versions of the same, insights may appear in seconds that might otherwise have taken years of painstaking experimental work. (The drug discovery and testing process still takes years, but maybe now it can start tomorrow for Chagas disease instead of in 2025.)

Illustration of RNA polymerase II ( a protein) in action in yeast. Image Credits: Getty Images / JUAN GAERTNER/SCIENCE PHOTO LIBRARY

Lest you think too much is resting on a computer’s prediction of experimentally unverified results, in another, totally different case, some of the painstaking work had already been done. John McGeehan of the University of Portsmouth, with whom DeepMind partnered for another potential use case, explained how this affected his team’s work on plastic decomposition.

“When we first sent our seven sequences to the DeepMind team, for two of those we already had experimental structures. So we were able to test those when they came back, and it was one of those moments, to be honest, when the hairs stood up on the back of my neck,” said McGeehan. “Because the structures that they produced were identical to our crystal structures. In fact, they contained even more information than the crystal structures were able to provide in certain cases. We were able to use that information directly to develop faster enzymes for breaking down plastics. And those experiments are already underway, immediately. So the acceleration to our project here is, I would say, multiple years.”

The plan is to, over the next year or two, make predictions for every single known and sequenced protein — somewhere in the neighborhood of a hundred million. And for the most part (the few structures not susceptible to this approach seem to make themselves known quickly) biologists should be able to have great confidence in the results.

Inspecting molecular structure in 3D has been possible for decades, but finding that structure in the first place is difficult. Image Credits: DeepMind

The process AlphaFold uses to predict structures is, in some cases, better than experimental options. And although there is an amount of uncertainty in how any AI model achieves its results, Hassabis was clear that this is not just a black box.

“For this particular case, I think explainability was not just a nice-to-have, which often is the case in machine learning, but it was a must-have, given the seriousness of what we wanted it to be used for,” he said. “So I think we’ve done the most we’ve ever done on a particular system to make the case with explainability. So there’s both explainability on a granular level on the algorithm, and then explainability in terms of the outputs, as well the predictions and the structures, and how much you should or shouldn’t trust them, and which of the regions are the reliable areas of prediction.”

Nevertheless, his description of the system as “miraculous” attracted my special sense for potential headline words. Hassabis said that there’s nothing miraculous about the process itself, but rather that he’s a bit amazed that all their work has produced something so powerful.

“This was by far the hardest project we’ve ever done,” he said. “And, you know, even when we know every detail of how the code works, and the system works, and we can see all the outputs, it’s still just still a bit miraculous when you see what it’s doing… that it’s taking this, this 1D amino acid chain and creating these beautiful 3D structures, a lot of them aesthetically incredibly beautiful, as well as scientifically and functionally valuable. So it was more a statement of a sort of wonder.”

Fold after fold

The impact of AlphaFold and the proteome database won’t be felt for some time at large, but it will almost certainly — as early partners have testified — lead to some serious short-term and long-term breakthroughs. But that doesn’t mean that the mystery of the proteome is solved completely. Not by a long shot.

As noted above, the complexity of the genome is nothing compared to that of the proteome at a fundamental level, but even with this major advance we have only scratched the surface of the latter. AlphaFold solves a very specific, though very important problem: given a sequence of amino acids, predict the 3D shape that sequence takes in reality. But proteins don’t exist in a vacuum; they’re part of a complex, dynamic system in which they are changing their conformation, being broken up and reformed, responding to conditions, the presence of elements or other proteins, and indeed then reshaping themselves around those.

In fact a great deal of the human proteins for which AlphaFold gave only a middling level of confidence to its predictions may be fundamentally “disordered” proteins that are too variable to pin down the way a more static one can be (in which case the prediction would be validated as a highly accurate predictor for that type of protein). So the team has its work cut out for it.

“It’s time to start looking at new problems,” said Hassabis. “Of course, there are many, many new challenges. But the ones you mentioned, protein interaction, protein complexes, ligand binding, we’re working actually on all these things, and we have early, early stage projects on all those topics. But I do think it’s worth taking, you know, a moment to just talk about delivering this big step… it’s something that the computational biology community’s been working on for 20, 30 years, and I do think we have now broken the back of that problem.”

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Why some scientists want to rebrand shark attacks as 'negative encounters' –



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Dropping the phrase “shark attack” is a great way to change the narrative about the much-maligned sea creatures, says marine scientist Toby Daly-Engel.

Last week, the Sydney Morning Herald reported that scientists in two Australian states are moving away from that term in favour of more neutral language, like “bites,” “incidents” or “negative encounters.”

The story drew swift mockery online, as well as backlash from an organization that represents people who have been injured by sharks

But Daly-Engel, director of the Florida Tech Shark Conservation Lab, says we’re long overdue for a language shift when it comes to the misunderstood ocean dwellers, which are at a greater risk from humans than vice versa.

Here is part of her conversation with As It Happens guest host Susan Bonner. 

What do you make of the Australian decision to rebrand shark attacks?

I think it’s a really good step in the right direction, because for a long time we’ve known that [with] shark attacks, it really depends on people, not on sharks. And so trying to rebrand these interactions in a way that more accurately represents the event is really good as far as we’re concerned.

But this is being mocked quite a bit, especially the suggested terminology, “negative” shark “encounters.” Isn’t a shark attack sometimes just a shark attack?

Actually, most shark attacks are what we call provoked, meaning they are instigated by humans. And so the notion of a shark attack kind of conjures an attack out of the blue by some sort of mindless, bloodthirsty predator. And in reality, that’s not it at all.

Most things that get labelled by the media as shark attack are things like people poking sharks underwater, chumming where people are swimming or doing other things that really create a situation where somebody might be hurt by a shark. 

But the vast majority of these interactions are not actually due to the shark. And so the notion of shark attack, even though it’s the most recognizable terminology, it’s really inaccurate.

I guess, though, if a shark is biting you, whether it’s being called an attack or an interaction isn’t really the first thing on your mind.

Sure. But at the same time, in general, sharks have, in reality, way more to fear from humans than we do from them. 

Shark attack[s are] monumentally rare, more rare than being bitten by someone from New York, statistically speaking. Whereas humans are — conservatively, this is an underestimate — we’re taking at least 100 million sharks out of the ocean every year.

And what we’re finding as scientists is that [sharks] … reproduce more slowly than we realized, even more slowly than people. And so many, many shark populations are really in trouble. And that’s not good because sharks as predators are really helpful for keeping the rest of the food items, the prey in the food web, in check and keeping them in balance.

The terminology may sound unnatural or silly to some people, but that’s because most people’s concept of what is a shark attack is really based on the rarest kind.– Toby Daly-Engel, marine scientist 

What kind of a difference do you believe this change of terminology could mean for how people view sharks?

I hope that it sheds light on the fact that sharks have more to fear from us than we do from them.

Like I said, the terminology may sound unnatural or silly to some people, but that’s because most people’s concept of what is a shark attack is really based on the rarest kind. 

Sharks are much more careful, much more fragile than people realize. They’re very long lived. Some species we now know can live over 400 years. They’re more likely to scavenge dead prey than they are to attack live prey, because their natural prey has things like spines and claws and beaks that can hurt them.

So when an attack occurs on a human, it’s because we are in their environment and they mistake us for a natural prey item, or they don’t know what we are and they go to figure it out with. Like dogs and babies, sharks can only really figure things out using their mouths.

A woman floating on the surface of the water in Compass Cay in the Exumas, as nurse sharks swim beneath her. Scientists say that despite pop culture depictions, most sharks are small to medium-sized. (Khaichuin Sim/Getty Images)

A spokesperson for a group representing people who have been bitten by sharks told the [Sydney Morning Herald] that he’s worried about “sanitizing” shark bites. What would you say to him?

I would say that shark attacks in general are going down per capita, even though the number of people that are in the water is going up. And that’s because we know we’ve lost up to 70 per cent of all sharks just in the last 50 years. And that is going to have grave consequences on our ocean health.

Anybody who likes the ocean, likes seeing fish in the ocean, all of that diversity is in danger without the predators. And most sharks are not at the top of the food chain. Most sharks are not what we think of as apex predators. There’s not that many massive ones. Most sharks are these cute little medium-sized things. They are both predator and prey. And without them, what we see is what’s called extinction cascade.

Considering you’re more likely to get struck by lightning … than bitten by a shark, considering you’re more likely to be killed by a vending machine than a shark, I think that there is very little chance of this type of measure minimizing shark attack. It has a much better chance of kind of helping people to understand that most of what the media calls shark attacks are really not the shark’s fault. They’re really just due to people.

Maybe we need some horror movies about vending machine attacks and New York City bite attacks.

I mean, just don’t shake them. Like, if you can’t get your chips out, just leave them there. That’s all I can say.

But after movies like Jaws and the innate fear that people have about sharks, is rebranding really going to make much of a difference here?

Even if there’s some mockery, there’s some silliness, regardless of this kind of attention, if it can help people understand the role that sharks play in the ecosystem and how mistaken our ideas are about shark attack, then, yeah, maybe it’ll do a little bit of good.

Sharks are feared. There are very few laws protecting them. And yet we know that these things grow more slowly and reproduce more slowly than just about any animal on Earth. And so they are incredibly in need of protection.

So every little bit can help because there’s not a lot of, you know, big movements out there for shark advocacy. There’s no such thing as shark-safe tuna, for instance. So I think because there is that fear, it’s even more important that institutions speak up on behalf of these animals, which are really, really important to the health of our planet’s oceans. 

Written by Sheena Goodyear. Interview produced by Chris Harbord. Q&A has been edited by length and clarity.

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