In recent years, there has been a lot of excitement about the potential for studying human diseases in what are called "organoids." These small patches of tissue, formed using stem cells, appear to produce many of the same cell types and at least some of the structures normally formed in actual organs, and thus can provide a better model for diseases that rely on the sometimes complex interactions among multiple specialized cell types that are a feature of the human body.

But even the most sophisticated organoids lack a lot of the features of a real human body. This is especially true for brain organoids, which don't form any of the connections with specialized brain structures needed to behave "normally." On Wednesday, a research group at Stanford University described a possible way to study brain organoids in a somewhat more natural context: They genetically wiped out a large portion of the mouse brain and replaced it with human brain organoid cells.

The replacements

Organoids, because they adopt a three-dimensional tissue structure and consist of various specialized cells, provide a much better model for an intact tissue than simply having a bunch of disassociated cells lying flat on a culture dish. But they still have a lot of limitations—they're not hooked up to a circulatory system that allows the liver to process chemicals the organoid produces and don't have immune cells moving through them, to give just a couple of examples.

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Our basic understanding of core-collapse supernovae hasn't changed in decades. Large stars burn through all the fuel at their cores and start creating heavier elements in reactions that consume energy. The lack of energy from these reactions allows gravity to pull the interior of the star in on itself, collapsing it into a neutron star or black hole. The energy released by this process then blows the remainder of the star apart.

And, generally, that's right. But there's an entire busload of devils in the details. The statistics of supernovae that we've observed indicate that the model may be seriously incomplete. And on the theoretical side, there are still plenty of uncertainties, including over some of the basics, such as whether all core collapses actually result in a supernova.

A paper being released by Physical Review D provides what might be a potential explanation for the discrepancy: flavor-changing neutrinos. Neutrinos play a key role in our current models of supernovae, and right now, those models don't take into account one of neutrinos' most striking features: their ability to change identity.

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The use of coal to generate electricity on the US grid has been plunging for nearly two decades, and the first Trump administration was unable to affect the trend. So his second administration has attempted more aggressive interventions to prop up coal use. One of its most direct means of doing so is to order coal plants that were scheduled to close to remain open, even if there is no need for them.

The administration's justification for these orders is a statute that allows the Department of Energy (DOE) to declare an emergency in the case of wartime or a sudden shortfall in generation. A number of parties, including states where coal plants have been slated to close, have challenged this declaration. And on Friday, in the first of these cases to make its way through the courts, the declaration was judged to be contrary to the statute. While this only affects a single coal plant in Michigan, the reasoning of the decision will apply to every coal plant closure that has been blocked by the DOE.

No emergency

The decision was issued by a unanimous three-judge panel from the DC Circuit's Court of Appeals. It focuses on the J.H. Campbell Generating Plant, which was scheduled to close last year but has been kept open by a total of five emergency declarations by the DOE, each limited to 90 days by the Federal Power Act. At issue was section 202(c) of that Act, which allows the DOE to declare emergencies when the US is at war or when “an emergency exists by reason of a sudden increase in the demand for electric energy, or a shortage of electric energy.”

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On Tuesday, Google announced AlphaGenome Atlas, a resource that attempts to predict the consequences of every possible single-base variant in the human genome. The human genome is about 3 billion bases long, so trying the other three DNA bases that don't appear in our reference genome means sending a total of 9 billion bases through AlphaGenome software.

AlphaGenome is designed to identify potential functions of non-coding DNA, which does not encode proteins but makes up the vast majority of the human genome. Some of this non-coding DNA is essential for controlling the activity of the protein-coding portion—it tells the cell where and when to make messenger RNAs, how to process them into mature protein-coding forms, and so on. But much of it appears to be little more than the remains of viruses and other molecular parasites.

Being able to identify the functional portion is very useful, as is having all the analysis done by a single software package. But until biologists start to use it heavily (assuming they do), it won't be clear what AlphaGenome offers beyond what we could have gotten out of its training data.

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Late last week, word started leaking that the National Institutes of Health (NIH) had reached an agreement with the Department of Defense that would see part of the NIH's budget used to fund research at the Department of Defense. So, on the Friday just prior to a US holiday weekend, the Department of Defense released a copy of the agreement and confirmed that it had been signed roughly a month earlier. The move is striking for a number of reasons, ranging from the existing budget disparities between the two parties involved to the fact that the money would be used for projects that the current NIH leadership has explicitly rejected.

The agreement itself sets up a system where the NIH would transfer money to the Department of Defense to fund staff and projects that would "support the advanced development of medical countermeasures against pandemic influenza, chemical, biological, radiological, and nuclear (CBRN) threats, and emerging infectious diseases." The money would come out of the budget for the NIH's National Institute of Allergy and Infectious Diseases, or NIAID, to which Congress has allocated $6.6 billion in 2026. The agreement is set to run for a decade.

Left unspecified is just how much of the NIAID budget will be spent on Defense projects. Reporting by Nature suggests that the Department of Defense was looking for up to a third of its total budget but was being told to settle for about 10 percent. There's obviously an enormous disparity between the budgets of these two agencies, given that the 2026 Defense budget is roughly $1 trillion. That budget is under considerable strain, however, due to the open-ended nature of the conflict with Iran. The deal has also been announced at a time when the NIH has been struggling to issue sufficient grants to use the money that Congress allocated to it.

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On Friday, researchers announced the completion of a map of every neuron in the brain of a male fruit fly. The "connectome" provides a tool that can accelerate neurobiology research. But it also provides an opportunity to do some science on its own, as the connectome of a female Drosophila had been completed earlier this year. The work also provided the team behind it the opportunity to refine tools that are likely to be applied to ever-more complex nervous systems, including (potentially) those of vertebrates.

The new work involved a collaboration between biologists at the Howard Hughes Medical Institute's Janelia Research Campus and computer scientists at Google—both acknowledge that neither could have done the project without the other. Preparation of an entire brain for imaging at the necessary resolution requires a distinct set of skills, as does interpreting what those images indicate. But building a complete picture of the hundreds of millions of synapses in a brain as small as the fruit fly's is a task that can't be achieved by humans in a manageable amount of time.

The people behind the effort expect that in the long term, the effort will be worth it, as the connectome could give neurobiologists a valuable tool for understanding how the brain works.

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On Tuesday, the House of Representatives passed a stopgap measure that would continue funding the US government through early December. While the measure still requires the signature of President Trump, it's widely expected that he will act to avoid a government shutdown immediately before the midterm elections.

This is a normal part of how the US government has operated in recent years, as it's often difficult to build the political support needed to pass a full year's budget in advance. In fact, dissent within the House's Republican caucus prevented them from agreeing on their own measure to keep the government open; instead, the House simply adopted a version of the spending bill that had previously passed the Senate.

From the perspective of scientists and their supporters, that adoption turned out to be a very good thing, because the Senate's budget bill, passed in early August, contains a provision that blocks the Office of Management and Budget (OMB) from implementing new rules that would give political appointees full control over what science is funded and allow them to cancel any grant at any time. The proposed rule has been widely decried as catastrophic for science, and it faced widespread opposition from scientific and health-focused organizations.

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The genetic code is what life everywhere uses to convert the information contained in DNA into specific protein sequences. With minor variations, the same genetic code is used by every living thing on Earth, suggesting it was already present in the last common ancestor of all of them. It's not an easy thing to change, because so many things in every cell depend on it.

Nevertheless, some preliminary steps have been taken. Researchers have managed to add some new amino acids to a bacterial cell and were able to make proteins that were one amino acid less than usual. But it's a slog; for some of this work, people have had to re-engineer every single gene in a bacterial genome.

Now, researchers have found a way to operate two separate genetic codes simultaneously, avoiding the need to do any work to compensate for altering the code that every protein in a cell relies on. They didn't test it in an actual cell, and it might cause some problems there. But it's a creative solution that should accelerate some synthetic biology work.

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On Thursday, a US District Court in Oregon ordered the US government to restart the process of approving wind projects. All new wind development in the US has been on hold since August 2025, when the Department of Defense (DoD) stopped participating in a process that allows it to compel developers to alter projects in order to limit their interference with radar equipment. The court ruled, however, that the DoD's national security claims did not allow it to opt out of a process that is legally mandated.

The Trump administration has made many attempts to block wind development, both offshore and land based. Its attempts to stop offshore wind included the same approach at issue in this case: Claim that drone developments mean that radar interference by wind turbines creates a national security risk. The courts were not sympathetic to this claim, including in cases where judges examined a classified report that the DoD was using to justify blocking offshore wind construction. As a result, the administration has turned to paying companies not to pursue wind development.

In parallel, the government was pursuing a similar approach for onshore wind. Here, a law lays out a process for the DoD to evaluate any problems posed by wind turbines and negotiate changes to planned wind farms with the developers. As laid out in the new decision, the government simply stopped participating in this process in August 2025, first by refusing to sign off on previously negotiated agreements, and later by refusing to draft agreements entirely. Eventually, it simply refused to participate in negotiations at all. This has brought a halt to all wind development in the US.

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A lot of the AI work in biology has been focused on designing proteins. That's partly because proteins do most of the business of life, catalyzing the interesting chemistry and structuring cells. So, figuring out how to make a new protein can mean directly tinkering with biochemistry, providing new and potentially useful functions.

Since the genetic code provides a layer of abstraction between DNA and proteins, it wasn't obvious what a model trained on DNA could do. Yet people went ahead and made a large genome model, and it turned out to be able to output DNA sequences that could encode functional proteins in bacteria and mimic the gene structures found in complex cells. Now, those same models have been used to output the genomes of viruses that infect bacteria.

This isn't science fiction—all the viruses the models created are closely related to an existing virus. But they do have some distinct features that would be challenging to evolve. And the researchers who did the work, based at Stanford University, suggest we may want to start thinking now about preparing for the potential that someone could develop a related AI that can design a virus that targets vertebrates.

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D-Wave is a bit of an oddity in the quantum computing space, having been founded back in the last century. And its initial offering wasn't a quantum computer like those being developed by IBM or Google. Instead, the company built what's now called a quantum annealer, a machine that isn't general-purpose but can solve a large class of optimization problems. While the hardware shares some similarities with the qubits used in gate-based quantum computers, it operates in a fundamentally different way.

But a few years back, D-Wave started working on gate-based hardware, apparently choosing a somewhat unusual qubit technology called fluxonium. And this year, the company acquired a startup called Quantum Circuits that spun out of Yale University and has been developing what's called a dual-rail qubit (the same technology used by Amazon), which promises to make most errors very easy to detect, simplifying error correction.

On Wednesday, the company is publishing a paper in Nature that describes a key step in validating this dual-rail technology, showing that two of the qubits can be entangled without altering their best feature: Most are a single type that is easy to detect.

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When the history of our ancestry is written, the fact that we've interbred with some of our closest relatives, the Neanderthals and Denisovans, will have a central role. And it will be tempting to write it as a very tidy story: Once we got genomes from these other groups, it was possible to identify the sequences in our genomes that we shared with them.

But in reality, as scientists started poking large enough collections of data, there were regular hints of some strange ancestry in our genomes. It was hard to pin down, though, at least in part because the 2 percent on average of Neanderthal DNA found in many populations does not guarantee that any two individuals will have the same 2 percent. So having the genomes of those two groups made sense of some things researchers had already been seeing.

But knowing what we do about Neanderthal and Denisovan DNA is now allowing researchers to answer a somewhat different question: Is there anything else? Using recently developed analytical techniques, they find evidence of a third lineage that we apparently interbred with before any modern humans left Africa. Again, there were hints of this earlier, but so far, there has been no genome from a modern human relative to help us understand the details—the source of this DNA remains a "ghost lineage."

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There are many algorithms for which it has been mathematically proven that a quantum computer can generate results that would take a classical computer an unreasonable amount of time to generate. Unfortunately, today's quantum computers either can't run those algorithms or can only run simplified versions that classical computers can also handle. This has left the field facing a challenging question: Can we demonstrate the promise of quantum computers on today's noisy, limited hardware?

That's a more difficult question than it may first appear. If you generate a result that's out of reach of today's regular computers, it may not be possible to verify that you got the right result. And given that today's quantum computers are somewhat error-prone, getting the wrong result is a distinct possibility. Further, in the absence of a mathematical proof of the capabilities of quantum hardware, it's possible that a better classical algorithm could outperform the quantum hardware.

These issues inspired IBM to launch a quantum advantage tracker. On Thursday, the company announced three new entries that it says clearly show a quantum advantage, each using a different approach to overcoming errors and validating quantum results. "Trusted computing when you can do classical simulations is irrelevant," IBM's Jay Gambetta told Ars. "Trusted computing when you can't do classical simulations is a big deal."

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If you follow quantum computing news for long enough, it can start to seem like any quantum system that can alternate between two well-separated energy states can be used as a qubit. Atoms, ions, photons, electrons, and manufactured devices all have their backers. One of the key things that attracts backers is the tech's ability to scale. We'll need a lot of high-quality qubits to start doing any complex computations, and the ability of any technology to get us there is the subject of debate.

So even as some technologies can now support thousands of qubits, some competitors are still working on a small handful of qubits—the companies behind them are convinced that they have the potential to scale more effectively.

One of those technologies involves quantum dots that hold a single electron. Their advantage is that we can manufacture them using the same tech we use to build traditional processors, an approach that has proven to be scalable. This week saw two new papers describing different ways of using quantum dots, one of which was appealing enough that IBM bought the company that developed it. Separately, another company has released a processor showing 100 individual electrons being held in diamond defects, technology that wasn't obvious could scale.

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A couple of decades after the discovery of systems that could selectively target DNA, we're starting to see the first therapies based on gene editing. One challenge these developments have faced is safety. While we can make them pretty specific to the gene we want edited, the human genome is very large, and even rare DNA sequences can appear a couple of times by chance.

As a result, all the original gene-editing systems had known rates of what are called off-target effects, in which they simply edit the wrong sequence. This may be a low-probability event, but edit enough cells—and therapies generally have to edit many—and errors become inevitable.

A lot of effort has gone into finding ways to minimize or eliminate off-target edits. In a recent issue of Nature, researchers described modifying the AI protein-folding software AlphaFold to help identify key areas of gene-editing proteins responsible for off-target effects. Those areas were then modified to reduce the problems.

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On Tuesday, the White House Office of Science and Technology Policy (OSTP) released a report entitled "Science: A New Golden Age," in which it lays out how it has viewed science, found it lacking, and believes the Trump administration is in the perfect position to fix things. It's a bit unexpected coming from an administration that has been proposing crippling funding cuts to research and trying to enable political appointees to terminate grants awarded based on scientific merit.

The report presents itself as the spiritual successor of "Science, the Endless Frontier," a policy document that laid out the case for government-funded science in the wake of World War II. The New Golden Age (SNGA) says that, while the concepts promoted by the original remain vital, the circumstances have changed such that we need major revisions to how the government is implementing things.

The result is an odd mix. It completely ignores or glosses over many things that the administration is doing to harm scientific progress. In some cases, it identifies issues that have already been discussed as problems within the scientific community, but misrepresents them. Elsewhere, it's a mixture of political grievances, ideas without a solid intellectual foundation, and an injection of Silicon Valley's perspective on innovation (Michael Kratsios, the director of the OSTP, formerly worked with Peter Thiel). As a result, it's unlikely to have anything like the impact of "Science, the Endless Frontier."

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Climate change is being driven largely by the greenhouse gases we've pumped into the atmosphere, which trap more of the Sun's energy there. That added energy increases the odds of extreme events: longer, more intense heat waves and droughts, interspersed with excessive precipitation. But these sorts of events have happened in the past—how can we tell if any given weather disaster has been made more likely by the climate?

It's a question with implications for everything from building codes to disaster preparedness. And there's some good news: According to a report released by the US National Academies of Science on Thursday, the field of climate attribution is growing increasingly mature and can answer some questions for us with far greater confidence than it could just a decade ago. The report also notes that there are still important limits and suggests steps to address them.

Overall, this makes it clear that climate attribution is normal, mainstream science. And the fossil fuel industry views that as a problem, as it could make it easier to hold companies liable for damages. This has triggered a backlash that has Republicans in Congress and state governments threatening the National Academies' funding.

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Most of the gas in the Universe is a mixture of hydrogen and helium. It's thought that the initial atmospheres of most planets also start out that way. However, over billions of years, as planets evolve, the composition of their atmospheres may shift. Hydrogen can react with other chemicals, and both it and helium can be lost to space. Venus, Earth, and Mars are thought to have second atmospheres, with their original hydrogen/helium envelopes having been lost and/or transformed.

The dynamics of loss are complicated. Lighter elements are lost more easily, but hydrogen can be protected by being incorporated into molecules like methane and ammonia. The gravity of the body can help retain some molecules, and a magnetic field can limit radiation's ability to blast material out of the atmosphere. Proximity to a star will matter too, both because of the radiation it produces and because it can heat the atmosphere and expand it to where gravity's influence is less substantial.

Given all these complications, it can be difficult to know what to expect to find on exoplanets. But a study in Wednesday's issue of Nature describes observations of helium being lost from the atmosphere of an exoplanet orbiting the star LHS 1140, about 50 light-years away. Based on the rate at which the helium is being lost, we can infer something about the remaining atmosphere.

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There are some obvious big picture issues that stand between us and useful quantum computing. Issues like whether we can make enough high-quality hardware qubits to connect into the error-corrected logical qubits we need, and how we generate the states needed to perform universal computation on those logical qubits. But there are also many less prominent challenges that will need to be solved before we can perform calculations.

One of those challenges, which only affects some types of hardware, is calibration. For devices we manufacture, like superconducting qubits, there are always subtle variations among individual qubits. (This is not true when we use something like an atom to hold the qubit, but the lasers that control them can drift.) As a result, this hardware is put through a process called calibration, where we test different frequencies and amplitudes of the microwave pulses that control them to find the combination that produces the lowest error rates, and then save those settings for use in calculations.

However, you can't perform the typical calibration process while you're doing calculations, which means drift becomes an issue for long and complicated algorithms. Google, though, has figured out that it's possible to do calibration using the same data that's used for error correction.

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One of the central features of plate tectonics is the formation of new crust at mid-ocean ridges. Part of the spreading process that drives continents apart, it was arguably the discovery of these ridges that drove widespread acceptance of plate tectonics as a theory. Thanks to decades of exploration, we now have a good picture of what the crust that forms at the site of spreading looks like. But we still have an incomplete idea of how its features are actually produced.

In other words, we have a good idea of the outcome of the process, but not a detailed picture of the process itself.

That is starting to change. In 2024, a team of French scientists was able to remotely monitor a major event on the border between the Australian and Antarctic plates, only two months after they installed equipment on the ocean floor. Their data shows that most of the spreading occurred in a relatively short time window, and some key events happened without any obvious seismic activity.

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