Around 600,000 years ago, humanity split in two. One group stayed in Africa, evolving into us. The other struck out overland, into Asia, then Europe, becoming Homo neanderthalensis – the Neanderthals. They weren’t our ancestors, but a sister species, evolving in parallel.
Neanderthals fascinate us because of what they tell us about ourselves – who we were, and who we might have become. It’s tempting to see them in idyllic terms, living peacefully with nature and each other, like Adam and Eve in the Garden.
If so, maybe humanity’s ills – especially our territoriality, violence, wars – aren’t innate, but modern inventions.
Biology and palaeontology paint a darker picture. Far from peaceful, Neanderthals were likely skilled fighters and dangerous warriors, rivalled only by modern humans.
Predatory land mammals are territorial, especially pack-hunters. Like lions, wolves and Homo sapiens, Neanderthals were cooperative big-game hunters. These predators, sitting atop the food chain, have few predators of their own, so overpopulation drives conflict over hunting grounds. Neanderthals faced the same problem; if other species didn’t control their numbers, conflict would have.
This implies that cooperative aggression evolved in the common ancestor of chimps and ourselves, 7 million years ago. If so, Neanderthals will have inherited these same tendencies towards cooperative aggression.
All too human
Warfare is an intrinsic part of being human. War isn’t a modern invention, but an ancient, fundamental part of our humanity. Historically, all peoples warred. Our oldest writings are filled with war stories. Archaeology reveals ancient fortresses and battles, and sites of prehistoric massacres going back millennia.
To war is human – and Neanderthals were very like us. We’re remarkably similar in our skull and skeletal anatomy, and share 99.7 percent of our DNA.
Prehistoric warfare leaves telltale signs. A club to the head is an efficient way to kill – clubs are fast, powerful, precise weapons – so prehistoric Homo sapiensfrequently show trauma to the skull. So too doNeanderthals.
Another sign of warfare is the parry fracture, a break to the lower arm caused by warding off blows. Neanderthals also show a lot of broken arms. At least one Neanderthal, from Shanidar Cave in Iraq, was impaled by a spear to the chest.
Trauma was especially common in young Neanderthal males, as were deaths. Some injuries could have been sustained in hunting, but the patterns match those predicted for a people engaged in intertribal warfare- small-scale but intense, prolonged conflict, wars dominated by guerrilla-style raids and ambushes, with rarer battles.
The Neanderthal resistance
War leaves a subtler mark in the form of territorial boundaries. The best evidence that Neanderthals not only fought but excelled at war, is that they met us and weren’t immediately overrun. Instead, for around 100,000 years, Neanderthals resisted modern human expansion.
The out-of-Africa offensive. (Nicholas R. Longrich)
Why else would we take so long to leave Africa? Not because the environment was hostile but because Neanderthals were already thriving in Europe and Asia.
It’s exceedingly unlikely that modern humans met the Neanderthals and decided to just live and let live. If nothing else, population growth inevitably forces humans to acquire more land, to ensure sufficient territory to hunt and forage food for their children.
But an aggressive military strategy is also good evolutionary strategy.
Instead, for thousands of years, we must have tested their fighters, and for thousands of years, we kept losing. In weapons, tactics, strategy, we were fairly evenly matched.
Neanderthals probably had tactical and strategic advantages. They’d occupied the Middle East for millennia, doubtless gaining intimate knowledge of the terrain, the seasons, how to live off the native plants and animals.
In battle, their massive, muscular builds must have made them devastating fighters in close-quarters combat. Their huge eyes likely gave Neanderthals superior low-light vision, letting them manoeuvre in the dark for ambushes and dawn raids.
Finally, the stalemate broke, and the tide shifted. We don’t know why. It’s possible the invention of superior ranged weapons – bows, spear-throwers, throwing clubs – let lightly-built Homo sapiens harass the stocky Neanderthals from a distance using hit-and-run tactics.
This wasn’t a blitzkrieg, as one would expect if Neanderthals were either pacifists or inferior warriors, but a long war of attrition. Ultimately, we won. But this wasn’t because they were less inclined to fight. In the end, we likely just became better at war than they were.
Alphabet-owned DeepMind may be best known for building the AI that beat a world-class Go player, but the company announced another, perhaps more vital breakthrough this morning. As part of its work for the 14th Critical Assessment of Protein Structure Prediction, or CASP, DeepMind’s AlphaFold 2 AI has shown it can guess how certain proteins will fold themselves with surprising accuracy. In some cases, the results were perceived to be “competitive” with actual, experimental data.
“We have been stuck on this one problem – how do proteins fold up – for nearly 50 years,” said Professor John Moult, CASP chair and co-founder, in a DeepMind blog post. “To see DeepMind produce a solution for this, having worked personally on this problem for so long and after so many stops and starts, wondering if we’d ever get there, is a very special moment.”
Researchers and enthusiasts across the internet have met the news enthusiastically, with some proclaiming that AlphaFold has solved the “protein solving problem.” But what does that mean, exactly? And how do we stand to benefit from it?
To start answering these questions, we need to take a closer look at the proteins themselves. As your biology teacher might have said, proteins are the building blocks of life, responsible for countless functions inside and outside the human body. Each one starts as a series of amino acids strung together into a chain, but it doesn’t take long — sometimes just milliseconds — before things start to get complicated. Some parts of the amino acid chain twist into helixes. Others fold back onto themselves as “sheets”. Before long, these helixes and sheets coalesce and contort into a protein’s final structure, and that’s what gives a protein the ability to perform specific tasks, like ferrying oxygen through your body or strengthening the structure of your bones.
In other words, shape is everything, and researchers have spent decades trying to find a way to determine a protein’s final, folded structure based solely on the amino acids that make up its backbone. That’s where CASP comes in — since 1994, the program has served as a focal point of sorts for teams around the world working to crack the protein solving problem with computational ingenuity. The rules are fairly simple: Every other year, organizers select a series of target proteins from a bevy of submissions whose structures have been determined experimentally, but haven’t been published yet. Researchers then get a few months to tune their systems and make their predictions, which are then judged by experts in the field for about a month after submissions are closed.
While CASP has been running for 26 years, it’s been in the past few that the scientific community has been able to bring quantum leaps in compute power and machine learning to bear on the challenge. In DeepMind’s case, that involved training AlphaFold 2’s prediction model on about 170,000 known protein structures, along with a vast number of protein sequences whose 3D structures haven’t yet been determined. This testing data, the team admits, is fairly similar to what it used in 2018, when the original AlphaFold system achieved top marks during CASP 13. (At the time, organizers hailed DeepMind’s “unprecedented progress in the ability of computational methods to predict protein structure.”)
That said, the team made some notable changes to its machine learning approach — they haven’t published a full paper yet, but the CASP 14 abstract book highlights some of their modifications. And beyond that, DeepMind also relied on about 128 of Google’s cloud-based TPUv3 cores, which ultimately gave AlphaFold 2 the ability to accurately determine a protein’s structure within just days, if not sooner — theNew York Times notes that, in some cases, predictions can be generated in a matter of hours.
This all sounds impressive — and it is, certainly — but there’s still plenty of work to be done. On the whole, AlphaFold’s results represented a dramatic improvement in accuracy compared to past years, and as mentioned, some of DeepMind’s predictions were accurate enough to rival experimental results at an atomic level. Others, however, fell short of that threshold. The company notes that “for the very hardest protein targets, those in the most challenging free-modelling category, AlphaFold achieves a median score of 87.0 GDT” — that’s just shy of the 90 GDT metric CASP co-founder Moult uses as the barrier for calling results “competitive” with real data. Put another way, DeepMind hasn’t fully solved the protein solving problem, but it’s getting closer than many had thought possible.
As DeepMind’s work continues, we’ll start to see the full extent of accurate protein prediction take shape — for now, the jury still seems out on what practical benefits we could expect to see in the short term. The company points to potential advances in sustainability and drug design as a result of its protein folding research, though it didn’t elaborate on specifics. Meanwhile, Janet Thornton, a structural biologist at the European Molecular Biology Laboratory-European Bioinformatics Institute, told Naturethat she hopes this leap in accuracy could shed light on the functions of “thousands” of unsolved proteins at work in the human body. If nothing else, though, researchers could be looking at a glut of new protein structure data to investigate, test against, and work backward from — that’s worth celebrating, even if we don’t know how it’ll be used yet.
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New research reveals the full details of how iron hydroxides form on a quartz substrate.
From the red hues in the Grand Canyon to the mundane rust attacking a neglected bicycle, iron hydroxides are all around us. They are even as common as quartz, the most widely distributed mineral on the planet.
Scientists know that iron hydroxides can capture heavy metals and other toxic materials, and that iron oxides also can be natural semiconductors. While these properties suggest many applications, the full details of how iron hydroxides form on a quartz substrate have been hidden in a “black box” of sorts—until now.
Young-Shin Jun, a professor of energy, environmental, and chemical engineering in the McKelvey School of Engineering at Washington University in St. Louis, has devised a way to open that box and observe the moment iron hydroxide forms on quartz. Her research appears in the journal Environmental Science & Technology.
Iron hydroxide formation
“This is telling the story of the birth of iron hydroxide,” Jun says.
When people speak of “formation,” typically they are talking about a substance growing. Before growth, however, there needs to be something to grow. Where does that first bit of iron hydroxide come from?
First, sufficient precursor elements need to be in place. Then the components can come together to form a stable nucleus that will go on to become a tiny solid particle of iron hydroxide, called a nanoscale particulate. The process is called solid nucleation.
Science has a firm grip on the sum of these two processes—nucleation and growth, together known as “precipitation”—and their sum has been used to predict iron hydroxide’s formation behavior. But these predictions have largely omitted separate consideration of nucleation. The results “weren’t accurate enough,” Jun says. “Our work provides an empirical, quantitative description of nucleation, not a computation, so we can provide scientific evidence about this missing link.”
This contribution opens many important possibilities. We can better understand water quality at acid mine drainage sites, reduce membrane fouling and pipeline scale formation, and develop more environmentally friendly superconductor materials.
X-rays and nucleation
Jun was able to look inside of the black box of precipitation by using X-rays and a novel experimental cell she developed to study environmentally relevant complex systems with plenty of water, ions, and substrate material, observing nucleation in real time.
Working at the Advanced Photon Source at Argonne National Laboratory in Lemont, Illinois, Jun employed an X-ray scattering technique called “grazing incidence small angle X-ray scattering.” By shining X-rays onto a substrate with a very shallow angle, close to the critical angle that allows total reflection of light, this technique can detect the first appearance of nanometer size particles on a surface.
The approach is so novel, Jun says, that when she discusses her lab’s work on nucleation, “People think we are doing computer modeling. But no, we are experimentally examining it at the moment it happens,” she says. “We are experimental observers. I can measure the initial point of nucleation.”
Her empirical method reveals that the general estimates scientists have been using overstate the amount of energy needed for nucleation.
“Iron hydroxide forms much more easily on mineral surfaces than scientists thought, because less energy is needed for nucleation of highly hydrated solids on surfaces,” Jun says.
Furthermore, having a precise value will also help improve reactive transport models—the study of the movement of materials through an environment. For instance, certain materials can sequester toxic metals, keeping them from entering waterways. An updated reactive transport model with more accurate nucleation information will have significant implications for water quality researchers working to better predict and control sources of pollution.
“Iron hydroxide is the main sequestration repository for these contaminants,” Jun says, “and knowing their origin is critical to predicting their fate.”
For high-tech manufacturing facilities, having a more precise understanding of how iron oxides or hydroxides form will allow for the more efficient—less wasteful—production of iron-based superconductors.
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