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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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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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's 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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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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In a widely expected move, a committee organized by the Food and Drug Administration (FDA) has voted to endorse removing restrictions on the manufacture of peptides for human use. Thursday's panel meeting saw a sharply divided group recommend lifting limits on four peptides; votes on three additional peptides are scheduled for today. The move comes despite a continuing lack of evidence regarding their safety and effectiveness.

The move had been telegraphed months earlier as peptide enthusiast and Health and Human Services Secretary Robert F. Kennedy Jr. took steps to ensure this outcome.

Like proteins, peptides are composed of amino acids that are chemically linked into a chain. Peptides differ merely by length; they're often 10–20 amino acids long, in contrast to proteins, which can be hundreds or thousands. Some of them, such as insulin, are specifically made by targeted processing of a protein into shorter fragments, and the resulting peptide interacts with receptors that have evolved to send signals to cells based on its levels.

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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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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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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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