This month we have both a good number of science announcements interesting enough to be included here and they are also so important that everyone should know about them.

• Let’s start with the one that will raise the most hackles. It’s a study of studies aimed at measuring the accuracy of gender stereotypes. The impolitic yet unsurprising finding is that they are mostly true in that people are generally accurate at assessing whether men or women were higher on some given trait or characteristic. That said, they underestimate gender differences in cognitive abilities and academic performance and overestimate them in personality traits and behaviors. Also worth noting that individuals were less accurate at these judgments than the group as a whole, a finding that is in line with similar studies.
• Next is a paper that seems especially relevant to us in Malaysia given recent news about health insurance costs. It discusses the rise of genetic testing and how the data thus obtained can be used to predict health outcomes. Yet many jurisdictions around the world have banned insurers from pricing their products using genetic data. This creates a mismatch between individuals who have undergone genetic testing and insurers that will only grow worse as genetic testing becomes more pervasive and more accurate. It is difficult to see how health insurance markets would continue to operate under such conditions.
• The major medical news this month has to be the so-called ‘tumor to pork’ announcement. It’s about a Chinese team inserting a pig gene into a virus and using it to infect cancerous cells. This then causes the body’s own immune system to treat the cancer as a foreign entity and attack it. The virus used, the Newcastle disease virus, is thought to be relatively harmless to humans though it is deadly to birds. The team used the technique to treat a variety of untreatable cancers including liver, ovarian, cervical etc. with excellent results.
• Finally a major discovery in the field of cosmology. Data from the James Webb Space Telescope reveals that around two-thirds of observable galaxies spin in one direction while remainder spin in the other. This contradicts the existing model of a homogenous universe in which more or less equal numbers of galaxies should spin in the opposite directions. One explanation that has caused excitement is that our own universe exists within a black hole and therefore was itself born rotating. This carries the implication that every black hole in our universe is a doorway to another baby universe. The less exciting explanation is that our own perception is skewed by the rotation of the Milky Way galaxy. I like the exciting explanation but in cases like this it’s usually the mundane one that holds true over the long term.

More science stuff this month and there’s been a lot of buzz about humanity getting close to the Singularity. I’m going to maintain a more skeptical attitude and curate only the more plausible news announcements.

• Let’s start with an announcement that is very exciting and has made headlines around the world as Microsoft no doubt intended, but has also left many physicists being skeptical. They claim that they now have a path towards viable quantum computers that work based on topological qubits. The qubits are built out of superconducting nanowire with each end being able to host a different topological state called a Majorana quasiparticle. They’ve also shown off a chip that they claim has eight such qubits. Other experts however say that there’s no proof in their paper that it works as claimed especially since the company has made similar claims before that were retracted later.
• Developments in the LLM space are too numerous to cover in detail so I’ll only focus on the big picture stuff. If you understand how the technology works, it’s always been a little strange why LLMs can do math at all. Through experiments on mid-sized LLMs, the writers of this paper claim that they do so using algorithms that no human would naturally use. Specifically they argue that numbers are stored in the LLMs as a generalized helix that is then manipulated to produce an answer. They show that even for simple arithmetic operations, the LLMs perform trigonometry to arrive at the answer. As LLMs grow increasingly big and sophisticated in capability, how they actually reason internally becomes ever more obscure as well, making this type of research critical.
• Next we have two papers on genetics. The first one suggests that severe depression and suicide ideation can be detected from blood markers. The tests are not identical for men and women as different metabolites are involved but the common thread is that they test for mitochondrial dysfunction. The researchers hypothesize that stress at the cellular level could overwhelm the body and trigger suicidal thoughts. The utility of using a blood test to detect severe depression are massive and so too would be the implication that treatments aimed at repairing metabolic function, including the use of supplements like folate and carnitine, could reduce the risk of suicide.
• Finally the last paper covers the increasingly powerful predictive power of genetic data when applied to health outcomes. It notes that in many countries, insurers are banned from using genetic information to price their insurance products. Yet nothing prevents individuals themselves from making insurance-purchasing decisions based on their own genetic profiles. If such practices were scaled up enough, it would effectively break the entire health insurance market.

Still not that much in the way of science news. I expect scientists along with many others are still reeling and adapting to changes being instituted by the new Trump administration.

• I’ve posted news about the GLP-1 weight loss drugs several times already but this one is about how they are already changing household consumption patterns. This study shows that households with at least one GLP-1 user are spending 6% less on groceries within six months of starting the drug. Furthermore the reductions are focused on lower purchases of calorie-dense, processed foods. Similar reductions are seen at fast-food chains and coffee shops. It’s no wonder that consumer goods companies are revamping their product lines and it would be fascinating to observe how far the changes go.
• Next we have a paper that discusses the use of the transformer architecture which powers the LLMs popular today to predict human brain states. Specifically they mean scans of the brain using fMRI. Their claim is that using 21.6 seconds of data, they can use transformers to predict the next 5.04 seconds. It’s unclear what this can be used for but philosophically it’s eerie to consider that a simple algorithm can predict the future state of your brain, and hence your thoughts and actions, based purely on past data.
• Finally here’s a paper that attempts to measure the rarity of truly exceptional people, or geniuses if you will. They focus on measuring three specific qualities, intelligence, conscientiousness and emotional stability with the intent of finding out how rare individuals who score high in all three measures are compared to the general population. It’s no surprise that these people truly are rare and even people who score above average on more than one of the variables are quite scarce. The suggestion is that organizations should place a higher priority on recruiting such exceptional talent.

Not many scientific announcements that seem worth including this month either but these two do have major implications if they pan out.

• This first one is likely the only one that has practical implications however. It pertains to the development of a probabilistic weather forecasting model that uses machine learning. Existing weather forecasts are deterministic physics-based simulations while previous attempts to create models that use machine-learning, or AI basically, have yielded less reliable and accurate forecasts. The authors of this paper claim that their new model beats traditional ones in both accuracy and speed. Named GenCast, it is able to generate 15-day global forecasts in 8 minutes. If true, this is obviously both amazing and immediately useful and only the tip of the iceberg of practical applications in the field of AI.
• Things are going on in the field of cosmology as the nature and existence of Dark Energy keeps running into problems. Now we have a new paper that threatens to completely upend this view of the universe. The new conception which the authors call the Timescape claim that since the distribution of matter throughout the universe is not homogenous, contrary to previous assumptions, that the passage of time is not homogenous as well. Specifically time passes faster in the voids where no matter is present and this may be enough to explain the phenomena of distant supernovae redshift without having to invoke Dark Energy as an explanation. I don’t have the qualifications to judge how seriously to take this paper but I’m sure all the cosmologists are talking about it right now.

Again not that much in the way of science news. The online discourse is being completely dominated by the fallout from the elections in the US.

• The most controversial story making the rounds this months is the one about a scientist who treated her own breast cancer by injecting lab-grown viruses into the tumor. The case itself isn’t particularly novel as the treatment known as oncolytic virotherapy is already being tested elsewhere. It involves using viruses to attack cancerous cells and provoke the immune system to fight them. In this case, the scientist in question, Beata Halassy, used two viruses, a measles virus and a vesicular stomatitis virus. What provoked controversy is that this treatment has obviously not been approved by any government regulator and so Halassy took matters into her own hands. As far as my concerned, it’s her body and her choice to make so there’s no question about this being ethical or not. In any case, it’s been working so far as this happened four years ago and the results are only being published now.
• There’s an ever growing body of evidence that the new class of weight-loss drugs, GLP-1 receptor agonists, offer an entire host of health benefits beyond just weight loss. I’m not arguing to the contrary of course but it’s also worth paying attention to the possible side effects and other consequences of long-term use. This new paper claims the use of these drugs also lead to substantial loss of muscle tissue. It hasn’t established that the loss is greater than what would be expected from the weight loss itself but it’s still a matter of some concern.
• Finally things are moving in the world of physics with regards to what dark energy actually is. I won’t go into the whole mess of why the concept of dark energy is needed to explain why the universe looks as it does as that is a whole other story. What’s new here is that we now have new data from the Dark Energy Spectroscopic Instrument (DESI). The instrument found that the density of dark energy seems to increase over time. This increase is consistent with formation of new black holes as massive stars die and is being interpretated as evidence in favor of the view that there is a fundamental coupling between dark energy and black holes. All this is far above my level of comprehension and as far as I can tell, this is only one of several other possible changes to how dark energy is being viewed. Still, it does seem that we must brace ourselves for some major revisions on what the current consensus in cosmology is.

Not much in the way of science news. I suppose the Nobel Prize announcements have a way of overshadowing things.

• Easily the most headline grabbing news this month is the announcement by a team in China that they have successfully cured a patient’s type 1 diabetes with stem-cell therapy. They took fat cells from the woman, induced them to behave as pluripotent stem cells and used these to create islet cells, the type of cells that create insulin in the pancreas. These were then injected back into the woman’s body between the skin and the abdominal muscles where they successfully engrafted. They claim that the woman no longer needed insulin injections around two and a half months after the procedure and remained so for a year afterwards. They’ve since tested this on two other patients and results are still pending. It’s an exciting result but it is still just one person for now and I found it weird that this woman is now apparently producing insulin from a part of her body that is not her pancreas?
• The next article is tough to understand, especially when it keeps using the term phonon laser and you don’t know what that means. This video from the always excellent Sabine Hossenfelder uses an easier to understand term for what they are: sound lasers. It’s not a new idea but it’s been difficult to get a sound source to achieve the required amplitude increases that remain coherent for long enough. This team uses the familiar approach of trapping a metallic ball with lasers but they also use an alternating electric field to amplify the sound vibrations inside the ball. The results are apparently impressive even though the experimental setup currently exists only in a vacuum and doesn’t actually create a beam as it opens a brand new field of possibilities.
• Finally, here’s one that I debated over including as it seems a little petty but it’s sound science and publishing it called for some courage. It examines the habit of gossiping among women and how it is used as part of intrasexual competition. In particular, it finds that although most people dislike malicious female gossipers, it is possible to frame the gossip as an expression of concern for the person being talked about. This reduced the negative social effects on the gossiper while being just as effective in harming the social reputation of the person being talked about.

October is the month in which the Nobel Prize committee announces its annual winners and they usually stagger out them out across several days. So this year, let’s do this in order.

The physiology or medicine prize goes to Victor Ambros and Gary Ruvkun for their discovery of microRNA. As most will know, our genes encode all of the information needed to construct every part of our bodies. Yet how does each part know to specialize and make only the specific proteins needed for that part? Different types of cells must be able to specialize by executing only the genetic instructions relevant to them.

Last year’s Nobel Prize in the same category was for the development of mRNA vaccines and indeed by the 1960s, scientists knew that mRNA was involved in the regulation of genes. Ambros and Ruvken, working on the now famous roundworm Caenorhabditis elegans discovered a short RNA molecule that did not code for any proteins but does inhibit the activity of another gene. They found that this microRNA turns off a specific gene by binding to a complementary sequence in its mRNA, thus proving the existence of an entirely new principle of gene regulation.

Their announcement initially didn’t make much of an impact it was thought that this mechanism was specific to C. elegans. It was since been shown that this form of gene regulation is universal among multicellular organisms, hence the award of this Nobel Prize.

Next the physics prize goes for the technological achievement that is foremost in everyone’s minds right now, yet is very much not physics. Instead it goes to two computer scientists who developed the artificial neural networks that are the basis of today’s AI. Recreating the neural networks in biological brains in the form of computer simulations was an obvious objective but early efforts were discouraging. Then in the 1980s, John Hopfield was inspired by his background in physics and devised a network with a property that is equivalent to the energy in the spin system found in physics. It can be trained to remember data and later retrieve them.

Upon learning of the Hopfield network, Geoffrey Hinton set out to improve them by adding a probabilistic element. He called his version the Boltzmann machine as it makes uses of the Boltzmann distribution in statistical mechanics, named after Ludwig Boltzmann. It consists of two types of nodes, visible nodes into which information is fed, and a hidden layer of other nodes. As the values in the nodes are updated one at a time, the pattern can change but the properties of the network as a whole remain the same. In this way, the machine can learn from being given examples to recognize similar traits in different things.

Both of these developments are foundational to the field of machine learning and led to the huge neural networks consisting of billions of nodes and arranged in multiple layers that power the LLMs that we know today. It’s still not physics but it’s probably the closest category the committee could think of for the discoveries that undoubtedly do deserve the prize.

The prize for chemistry also goes for AI, or at least close enough. Proteins are the building blocks of biology and they consist of strings of amino acids twisted and folded together into three-dimensional structures. It is the specific structure that they have that gives them their unique properties and while the shape is theoretically predictable, the large number of ways that a given string of amino acids can fold makes it an overwhelmingly difficult problem. That’s where the computers come in.

Demis Hassabis co-founded DeepMind and developed AI models to play boardgames. The company was later bought by Google and its AI was improved until it was able to beat the world champion at playing Go. Their true goal however was always to predict protein structures and their AlphaFold model achieved an accuracy that beat the best humans but still fell far short of what was needed. Then DeepMind hired John Jumper who applied the transformers architecture of neural networks to the problem and managed to obtain results almost as good as X-ray crystallography.

David Baker too participated in the same competitions to predict protein structures and he made his own piece of software Rosetta to do so. Then he realized that he could also use Rosetta to work in reverse, allowing a user to specify the desired protein structure and obtain suggestions on the needed amino acid sequence. To test its effectiveness, they created an entirely new protein structure, obtained the amino acid sequence from the software and then made the novel protein structure. They then used X-ray crystallography to confirm that its structure matched what they had specified.

The economics prize is awarded for engaging with the question of why some countries or societies are so much richer than others. Daron Acemoglu, Simon Johnson and James Robinson jointly published the seminal paper The Colonial Origins of Comparative Development that divided the institutions established by European colonizers into two types: inclusive ones and extractive ones. One key factor was the density of the indigenous population at the time. In more populous places or o colonies with a high rate of settler mortality, due to the Europeans being poorly adapted to local diseases, the colonizers exploited the local supply of labor, creating extractive institutions.

In less populous places, the Europeans themselves moved in to settle there and in turn built more equitable, inclusive institutions. The authors call the result a reversal of fortune as the more populous and prosperous societies fell behind the newly established ones that promoted long-term prosperity. Even after the end of colonization, local elites simply took over the extractive institutions and had no interest in transitioning to a more equitable society. While this was an undeniably influential paper, it’s also a contentious one and historians for example question the neat division of extractive and inclusive institutions.