I tried reading it but it was too stuffy for me. Do you think that corporations should be paying a higher tax rate than they are now in Canada? Should the corporate tax rate be higher than peoples personal tax rates?

‘Evolution Gone Crazy’: What Makes Sea Dragons So Strange



Among the ocean’s menagerie of bizarre creatures, sea dragons stand out. Relatives of sea horses and pipefish, sea dragons have long narrow snouts that they use like a straw to suck up meals of microscopic crustaceans. Instead of scales, the fish are covered in bony armor, and their backbones are kinked. Like their sea horse cousins, male sea dragons gestate a female’s fertilized eggs in a pouch.

They come in two groups of species, leafy and weedy. “Leafies” have elaborate branching appendages that make them virtually indistinguishable from the floating seaweed in their Southern Australian habitats. Weedy sea dragons are more streamlined but are also more colorful, with purple stripes and yellow polka dots.

Bill Cresko at the University of Oregon studies sea dragon genetics to answer one fundamental question: He and his colleagues want to know “how the hell” these fish came to look the way they do.

“We’re just really fascinated by, ‘How can you have an organism that looks like that? What has changed in the genome?’” he said.

A study published in June in the Proceedings of the National Academy of Sciences tried to answer these questions. Researchers sequenced the genomes of leafy and weedy sea dragons and compared them with other fish.

The strange appearance of sea dragons made the team think that there might have been something unusual happening with their fibroblast growth factor genes, “which are really important for development of things like teeth, which they don’t have, or the shape of faces or appendage outgrowth, to name just a few,” said Susie Bassham, a researcher in Dr. Cresko’s lab and an author of the paper.

Troubling Rise in Suicides Linked With Common Food Preservative

A recent increase in fatal sodium nitrite poisonings has some health experts calling for stricter regulation of the substance. Sodium nitrite is a white salt commonly used in curing meat. But in recent years, it’s also being used as a poison in suicides.

Ontario has seen at least 28 sodium nitrite poisoning deaths between 1980 and 2020, with most happening in the last two years of that period. Alberta Health Service’s poison center, which also serves the Northwest Territories and Saskatchewan, saw at least two sodium nitrite poisonings causing serious harm last year, and two more this year.

These numbers are likely an undercount because Canada does not collect comprehensive data about sodium nitrite poisoning.

Toxicovigilance Canada, a poison control network led by Health Canada, was unable to share national figures, citing “no national data from medical examiners, coroners or poison centers.” Poison centers also do not have a clear picture of the problem because it’s not mandatory for healthcare workers to pass along information about the poisonings they treat.

In the United States, the National Poison Data System recorded 47 cases of sodium nitrite poisoning between 2015 and 2020. Like in Ontario, most of these poisonings occurred in 2019 and 2020.

Online forums promoting poisonings

Sodium nitrite poisoning was virtually unheard of until very recently, says Eric McGillis, a Calgary-based medical toxicologist at Alberta’s Poison and Drug Information Service. However, “the cases are increasing exponentially over the last several years.”

The trend appears to be driven by online forums detailing how to dose sodium nitrite for suicide.

The Moon isn’t necessarily there if you don’t look at it. So says quantum mechanics, which states that what exists depends on what you measure. Proving reality is like that usually involves the comparison of arcane probabilities, but physicists in China have made the point in a clearer way. They performed a matching game in which two players leverage quantum effects to win every time—which they can’t if measurements merely reveal reality as it already exists.

“To my knowledge this is the simplest [scenario] in which this happens,” says Adan Cabello, a theoretical physicist at the University of Seville who spelled out the game in 2001. Such quantum pseudotelepathy depends on correlations among particles that only exist in the quantum realm, says Anne Broadbent, a quantum information scientist at the University of Ottawa. “We’re observing something that has no classical equivalent.”

A quantum particle can exist in two mutually exclusive conditions at once. For example, a photon can be polarized so that the electric field in it wriggles vertically, horizontally, or both ways at the same time—at least until it’s measured. The two-way state then collapses randomly to either vertical or horizontal. Crucially, no matter how the two-way state collapses, an observer can’t assume the measurement merely reveals how the photon was already polarized. The polarization emerges only with the measurement.

That last bit rankled Albert Einstein, who thought something like a photon’s polarization should have a value independent of whether it is measured. He suggested particles might carry “hidden variables” that determine how a two-way state will collapse. However, in 1964, British theorist John Bell found a way to prove experimentally that such hidden variables cannot exist by exploiting a phenomenon known as entanglement.

Two photons can be entangled so that each is in an uncertain both-ways state, but their polarizations are correlated so that if one is horizontal the other must be vertical and vice versa. Probing entanglement is tricky. To do so, Alice and Bob must each have a measuring apparatus. Those devices can be oriented independently, so Alice can test whether her photon is polarized horizontally or vertically, while Bob can cant his detector by an angle. The relative orientation of the detectors affects how much their measurements are correlated.

Bell envisioned Alice and Bob orienting their detectors randomly over many measurements and then comparing the results. If hidden variables determine a photon’s polarization, the correlations between Alice’s and Bob’s measurements can be only so strong. But, he argued, quantum theory allows them to be stronger. Many experiments have seen those stronger correlations and ruled out hidden variables, albeit only statistically over many trials.

This is the question that researchers at Columbia Engineering posed to a new AI program. The program was designed to observe physical phenomena through a video camera, then try to search for the minimal set of fundamental variables that fully describe the observed dynamics. The study was published on July 25 in Nature Computational Science.

The researchers began by feeding the system raw video footage of phenomena for which they already knew the answer. For example, they fed a video of a swinging double pendulum known to have exactly four "state variables"—the angle and angular velocity of each of the two arms. After a few hours of analysis, the AI produced the answer: 4.7.



"We thought this answer was close enough," said Hod Lipson, director of the Creative Machines Lab in the Department of Mechanical Engineering, where the work was primarily done. "Especially since all the AI had access to was raw video footage, without any knowledge of physics or geometry. But we wanted to know what the variables actually were, not just their number."

The researchers then proceeded to visualize the actual variables that the program identified. Extracting the variables themselves was not easy, since the program cannot describe them in any intuitive way that would be understandable to humans. After some probing, it appeared that two of the variables the program chose loosely corresponded to the angles of the arms, but the other two remain a mystery.

"We tried correlating the other variables with anything and everything we could think of: angular and linear velocities, kinetic and potential energy, and various combinations of known quantities," explained Boyuan Chen Ph.D., now an assistant professor at Duke University, who led the work. "But nothing seemed to match perfectly." The team was confident that the AI had found a valid set of four variables, since it was making good predictions, "but we don't yet understand the mathematical language it is speaking," he explained.

After validating a number of other physical systems with known solutions, the researchers fed videos of systems for which they did not know the explicit answer. The first videos featured an "air dancer" undulating in front of a local used car lot. After a few hours of analysis, the program returned eight variables. A video of a lava lamp also produced eight variables. They then fed a video clip of flames from a holiday fireplace loop, and the program returned 24 variables.

A particularly interesting question was whether the set of variable was unique for every system, or whether a different set was produced each time the program was restarted.

"I always wondered, if we ever met an intelligent alien race, would they have discovered the same physics laws as we have, or might they describe the universe in a different way?" said Lipson. "Perhaps some phenomena seem enigmatically complex because we are trying to understand them using the wrong set of variables. In the experiments, the number of variables was the same each time the AI restarted, but the specific variables were different each time. So yes, there are alternative ways to describe the universe and it is quite possible that our choices aren't perfect."

“Essentially, you can think of it as covering the entire protein universe. It includes predictive structures for plants, bacteria, animals, and many other organisms, opening up huge new opportunities for AlphaFold to have an impact on important issues, such as sustainability, food insecurity, and neglected diseases,” said Demis Hassabis, DeepMind’s founder and chief executive.

Scientists are already using some of its earlier predictions to help develop new medicines. In May, researchers led by Prof Matthew Higgins at the University of Oxford announced they had used AlphaFold’s models to help determine the structure of a key malaria parasite protein, and work out where antibodies that could block transmission of the parasite were likely to bind.

“Previously, we’d been using a technique called protein crystallography to work out what this molecule looks like, but because it’s quite dynamic and moves around, we just couldn’t get to grips with it,” Higgins said. “When we took the AlphaFold models and combined them with this experimental evidence, suddenly it all made sense. This insight will now be used to design improved vaccines which induce the most potent transmission-blocking antibodies.”