Who doesn’t hate spam emails and wishes that somebody would just come up with some better filters for it already? Although we’ve seen a decent amount of improvements when it comes to this issue these last few years, spam still often ends up in your inbox and it can get pretty annoying at times. The reason for a company’s inability to find a satisfactory solution to this problem like comes from their methods used to combat spam. Google, Yahoo, and other companies that offer email services decide who sends spam emails by looking at their history. If someone has a track record of sending emails that were marked as spam by their receivers, then the companies might add these senders to their “black lists” so that any further emails they send will end up directly in the spam folder.
This system works pretty well all things considered, but it’s hard to keep track of all the spammers and update said lists seeing as how new spammers who don’t have a previous track record surface on a daily basis. Needless to say, the companies offering email services should perhaps consider implementing a new and improved system instead of simply updating their current one. As fate would have it, a better email spam filter could be developed with inspiration drawn from the animal kingdom of all places. Actually, a lot of our technological improvements have been inspired by nature, so when biology professor Deborah M. Gordon from Stanford University suggests that ants could be the key to creating a better spam filter, I think we should at least take the idea into consideration, if only for the fact that it sounds very intriguing to say the least.
Ant colonies have some pretty interesting ways of deciding who their friends and foes are and it works a lot like our own immune system. Specifically, ants are able to recognize friends and foes just like cells are able to recognize potential pathogens. The method by which they accomplish this differs a bit given that ants use scent to achieve this feat, but the main principle is very similar. An ant knows its colony friends because it grew up with them and marks their scent as friendly. By comparison, when the ant leaves the colony and runs into a foe it marks its scent as unfriendly. However, a single ant can’t possibly remember all the friends and foes it encounters so it has to rely on the rest of its colony for the system to work. All ants use the same system and together they form something called a distributed decision network.
As mentioned, a single ant doesn’t recognize all the colony’s foes, but it does know a few of them and so does every other ant in the colony. Basically, the colony needs to act as a whole and each member needs to trust and join in when others spot an enemy and begin to attack. This system is certainly very successful for ants, but professor Gordon says that we might be able to make good use of it ourselves. Gordon has been working with computer scientist Fernando Esponda to develop a better email spam filter based on the very same distributed decision network system described above. “It’s an arms race,” says Gordon. “But a distributed decision network, similar to how ants and immune cells operate, might be a better defense against hackers, because they can’t simply penetrate the central system’s code.” Let’s hope that a spam filter based on this idea ends up being put into practice in the near future.
The First 3D-Printed Vegan Salmon Is In Stores
Revo Foods’ “THE FILET – Inspired By Salmon” salmon fillet may be the first 3D-printed food to hit store shelves. said that firm CEO Robin Simsa remarked, “With the milestone of industrial-scale 3D food printing, we are entering a creative food revolution, an era where food is being crafted exactly according to customer needs.”
Mycoprotein from filamentous fungi is used to make the salmon alternative and other meat substitutes. Vitamins and omega-3 fatty acids are in the product, like in animals. Is high in protein, at 9.5 grams per 100 grams, although less than conventional salmon.
Revo Foods and Mycorena developed 3D-printable mycoprotein. Years of research have led to laser-cooked cheesecakes and stacked lab-grown meats.
One reason for this push is because printed food alternatives may make food production more sustainable, which worries the fishing sector. Overfishing reduces fish populations in 34% of worldwide fish stocks.
Over 25% of worldwide greenhouse gas emissions come from food production, with 31% from livestock and fish farms and 18% from supply chain components including processing and shipping. According to Revo Foods’ website, vegan salmon fillet production consumes 77 to 86% less carbon dioxide and 95% less freshwater than conventional salmon harvesting and processing.
The salmon alternative’s sales potential is unknown. In order to succeed, Revo Foods believes that such goods must “recreate an authentic taste that appeals to the flexitarian market.”
The commercial distribution of 3D-printed food could change food production.
Open-source Microsoft Novel protein-generating AI EvoDiff
All diseases are based on proteins, natural molecules that perform vital cellular functions. Characterizing proteins can reveal disease mechanisms and ways to slow or reverse them, while creating proteins can lead to new drug classes.
The lab’s protein design process is computationally and human resource-intensive. It involves creating a protein structure that could perform a specific function in the body and then finding a protein sequence that could “fold” into that structure. To function, proteins must fold correctly into three-dimensional shapes.
Not everything has to be complicated.
Microsoft introduced EvoDiff, a general-purpose framework that generates “high-fidelity,” “diverse” proteins from protein sequences, this week. Unlike other protein-generating frameworks, EvoDiff doesn’t need target protein structure, eliminating the most laborious step.
Microsoft senior researcher Kevin Yang says EvoDiff, which is open source, could be used to create enzymes for new therapeutics, drug delivery, and industrial chemical reactions.
Yang, one of EvoDiff’s co-creators, told n an email interview that the platform will advance protein engineering beyond structure-function to sequence-first design. EvoDiff shows that ‘protein sequence is all you need’ to controllably design new proteins.
A 640-million-parameter model trained on data from all protein species and functional classes underpins EvoDiff. “Parameters” are the parts of an AI model learned from training data that define its skill at a problem, in this case protein generation. The model was trained using OpenFold sequence alignment data and UniRef50, a subset of UniProt, the UniProt consortium’s protein sequence and functional information database.
Modern image-generating models like Stable Diffusion and DALL-E 2 are diffusion models like EvoDiff. EvoDiff slowly subtracts noise from a protein made almost entirely of noise to move it closer to a protein sequence.
Beyond image generation, diffusion models are being used to design novel proteins like EvoDiff, create music, and synthesize speech.
“If there’s one thing to take away [from EvoDiff], I think it’s this idea that we can — and should — do protein generation over sequence because of the generality, scale, and modularity we can achieve,” Microsoft senior researcher Ava Amini, another co-contributor, said via email. “Our diffusion framework lets us do that and control how we design these proteins to meet functional goals.”
EvoDiff can create new proteins and fill protein design “gaps,” as Amini noted. A protein amino acid sequence that meets criteria can be generated by the model from a part that binds to another protein.
EvoDiff can synthesize “disordered proteins” that don’t fold into a three-dimensional structure because it designs proteins in “sequence space” rather than structure. Disordered proteins enhance or decrease protein activity in biology and disease, like normal proteins.
EvoDiff research isn’t peer-reviewed yet. Microsoft data scientist Sarah Alamdari says the framework needs “a lot more scaling work” before it can be used commercially.
“This is just a 640-million-parameter model, and we may see improved generation quality if we scale up to billions,” Alamdari emailed. WeAI emonstrated some coarse-grained strategies, but to achieve even finer control, we would want to condition EvoDiff on text, chemical information, or other ways to specify the desired function.”
Next, the EvoDiff team will test the model’s lab-generated proteins for viability. Those who are will start work on the next framework.
Chinese Dinosaur Might Have Been as Iridescent as a Hummingbird
Earlier this month, I wrote an article on a toy line of scientifically accurate Velociraptor action figures with plumage inspired by modern birds. I mused how impressive it would be if prehistoric raptors had been covered by feather patterns not unlike those in the toy line. Little did I know that two weeks later, researchers would reveal that some theropods had iridescent feathers that outshine David Silva’s velocifigures.
The Caihong juji, Mandarin for “rainbow with a big crest” (or just Caihong for short), was a “paravian theropod,” a clade commonly known for its winged forelimbs (even though many weren’t capable of flight) and enlarged sickle foot claws. In 2014, a farmer in the Qinlong County in the Hebei Province of Northeastern China gave a nearly complete Caihong fossil, feathers included, to The Paleontological Museum of Liaoning. Finding a complete skeleton is rare in paleontology and proved very helpful to the researchers. However, you might wonder just how scientists were able to determine the iridescent nature of the Caihong’s plumage. Two words: fossilized melanosomes.
Melanosomes are organelles that create, store, and transport melanin, which determines the pigments/colors of animal hair, fur, skin, scales, and feathers. Upon examining the Caihong’s head, crest, and tail feathers with an electron microscope, scientists discovered platelet-shaped structures similar in shape to the melanosomes that give hummingbirds their iridescent coloring. The rest of the body feathers had melanosome structures similar to those in the grey and black feathers of penguins, which would have made for an odd sight: a duck-sized dinosaur with body feathers as drab as a raven’s and head and neck feathers more colorful than a peacock’s.
The inferred feather coloration of the Caihong is not its only unusual feature, though. The dinosaur had longer arm and leg feathers than its relatives, and its tail feathers created a “tail surface area” that was larger than the famous proto-bird the Archaeopteryx. Furthermore, the Caihong had bony crests, which while common among most dinosaurs, are almost unheard of among paravian theropods. But, more importantly, it had proportionally long forearms, which is a feature of flight-capable theropods, even though scientists believe the Caihong didn’t fly. While this dinosaur apparently has the earliest examples of proportionally long forearms in the theropod fossil records, it still falls in line with the belief that the evolution of flight-capable feathers outpaced the evolution of flight-capable skeletons. The melanosomes, however, are the more intriguing discovery, since they are the earliest examples of “organized platlet-shaped nanostructures…in dinosaurian feathers.”
While paleontologists are confident the Caihong’s platelet structures are melanosomes, the researchers understand that their discovery is based partially on inference and could potentially be incorrect. If the structures aren’t melanosomes, well, that invalidates this entire article. But that’s what paleontology is all about: examining the evidence, creating inferences supported by that evidence, and changing those inferences when new information becomes available. Still, the concept of dinosaurs with iridescent feathers is pretty cool. If you want to learn more about the Caihong juji, you can read the original article on Nature.
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