After years of requesting tiny samples of lunar soil, plant scientists at the University of Florida were finally granted 12 grams to work with (out of the 382 kg brought back during the Apollo missions). They had proposed a simple experiment – could seeds germinate and plants grow in lunar soil? It turns out the answer is yes, sort of.

The researchers used Arabidopsis, or rockcress, which is a genus that contains the first plant to have its entire genome sequenced and is therefore a favorite of plant biologists.  They added nutrient rich water to one gram pots of lunar soil and planted Arabidopsis seeds in them. As controls they planted the same seeds with the same nutrients in regular soil, and simulated lunar and Martian soil, plus Earth soil but from extreme environments. All of the seeds sprouted. For about the first six days the plants all seemed to be doing equally well, but then it became clear that the plants growing in lunar soil were smaller, more varied in size, and were showing signs of stress.

The experiment was therefore a partial success – the plants grew surprisingly well but did not thrive in the lunar soil. Because they used Arabidopsis, they were able to also track gene expression in the plants. The plants growing in lunar soil had increased expression of genes related to stress, reinforcing the conclusion that there is something about the lunar soil that is not friendly to the plants, causing them to react as if they were growing in an extreme environment.

Interestingly, the researchers had two different type of lunar soil from different locations. One type is referred to as “mature” lunar soil, which was exposed directly to the solar wind. They also had not mature lunar soil, in which the plants fared a little better.

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I am happy to announce that pre-orders are open for my upcoming book, The Skeptics’ Guide to the Future, which will be released by Grand Central Publishing on September, 27th. You can preorder your book here.

This was a particularly fun book to write, with my two brothers, Bob and Jay (who also co-host the SGU podcast with me). This is our second book, with Evan and Cara also contributing to the first one (The Skeptics’ Guide to the Universe). In this new book we explore futurism itself – what have we learned from past attempts at predicting the future and how can we use those lessons to perhaps do a little better? We explore, for example, what I call “futurism fallacies”, common errors in trying to extrapolate our world into a vision of the future. One common fallacy is to extrapolate current trends indefinitely into the future, even though this is generally not the path that history has taken. Disruptive technologies, changing priorities, the interaction among various types of technology, and evolving culture all introduce zigs and zags into the course of history, and therefore the future.

Is futurism, therefore, doomed to failure? This is actually a matter of scholarly debate, with critics and advocates. Overall I think predicting the future is similar to predicting the weather – while it is impossible to predict the details beyond a very short window, we can make broad predictions about the climate. Similarly we can say that technology will not only continue to advance but the pace of that advance is accelerating. We explore those individual technologies that are just emerging and most likely to have a profound impact on our future, such as genetic engineering, additive manufacturing, artificial intelligence, and metamaterials. There are also some established technologies that will continue to advance, expanding into new niches, such as robotics.

We also discuss technologies that are just in the conceptual stage, and give our opinion as to whether or not they are likely to ever come to fruition. We will likely have fusion power someday, but I doubt we will ever have a space elevator (at least not on Earth).

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There are a lot of complexities to this case, as you might imagine. Some question whether or not Holmes, CEO of the now disgraced Theranos company that claimed it had revolutionized blood testing, was unfairly targeted because she is a woman. Her defense was also complex, including a claim she was abused by her boyfriend. These details are, of course, important in the pursuit of individualized justice. But I want to focus on some big picture factors – what might the results of this case mean?

I first wrote about Theranos in 2016 – I recognized the story as a skeptical cautionary tale. The claims that Holmes was making were implausible in the extreme. She claims her company innovated the technology to perform hundreds of different blood tests with a very small amount of blood and within a short period of time. The public is used to such advances in technology, and this claim, while bold, may seem plausibly incremental. However, medical experts recognized the claim for the nonsense it was. Far from being incremental, such a feat would have required hundreds of scientific breakthroughs all brought to technological fruition in a marketable product. This kind of advance does not come out of nowhere, without a paper trail of scientific research behind it.

Holmes was counting on a general level of scientific illiteracy, specifically to how the process of science works. It is increasingly difficult to make a major discovery or technological advance without all the groundwork being laid by incremental research spread out among various experts and institutions. Often when we hear of a new technology hitting the market, there is 20-30 years of background research. The idea for an mRNA vaccine started in the 1980s, for example. The new medical technologies that are coming online in the last decade have roots that go back decades.

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If someone gets seriously ill from COVID, to the point that they need to be hospitalized and even placed in ICU, and they were unvaccinated, how much should we blame them for their illness? This question can have practical implications, if we base decisions on allocating limited resources and insurance coverage of vaccine status. I wrote about this dilemma recently on Science-Based Medicine (and then discussed it on the SGU), and it sparked a lively discussion. Some of the responses amounted to justification for blaming the victim, which is essentially the core of the issue, and an important concept for activist skeptics to handle.

Blaming the victim can occur in many contexts. Within skeptical circles the most common manifestation is to blame people for being gullible (which is essentially the opposite of being skeptical). If someone, for example, falls for an obvious con it is easy to feel contempt or even anger toward that person for their gullibility. Sometimes gullibility is combined with scientific illiteracy. There are numerous pseudoscientific products on the market that require someone to have essentially no idea how the world works in order to believe the claims (or alternatively to compartmentalize any thoughts of mechanism of action). There are products that claim to improve the taste of wine simply by waving a plastic card over the glass, or to improve your athletic performance because you wear a small piece of rubber on your wrist – imbued with “frequencies” that harmonize with your body’s natural rhythms. There are fuel additives or devices that claim to dramatically improve the fuel efficiency of your car without any downside. And of course there are endless free energy devices that “they” don’t want you do know about.

It’s easy to write all this off as “caveat emptor” – if people pay a small price for their gullibility and scientific illiteracy, that is perhaps how it should be. We can then congratulate ourselves on being less gullible and more knowledgeable. Or we may moralize about individual responsibility, touting the fact that we invested the time to learn how to protect ourselves in a world full of con artists and scams. Blaming the victims of scams gives us the illusion of control (we can protect ourselves) and serves our sense of justice (people largely deserve what they get). But is this sort of blaming the victim morally or intellectually justified?

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Placing “self-replicating” and any kind of “bots” in the same sentence immediately raises red flags, conjuring the image of reducing the surface of the world to gray goo. But that is not a concern here, for reasons that will become clear. There is a lot to unpack here, so let’s start with what xenobots are. They are biological machines, little “robots” assembled from living cells. In this case the source cells are embryonic pluripotent stem cells taken from the frog species Xenopus laevis. Researchers at the Allen Discovery Center at Tufts University have been experimenting with assembling these cells into functional biological machines, and have now added self-replication to their list of abilities.

Further, these xenobots replicate in a unique way, by what is known as kinematic self-replication. This is the first instance of this type of replication at the cell or organism level. The researchers point out that life has many ways of replicating itself: “fission, budding, fragmentation, spore formation, vegetative propagation, parthenogenesis, sexual reproduction, hermaphroditism, and viral propagation.” However, all these forms of self-replication have one thing in common – they happen through growth within or on the organism itself. By contrast, kinematic self-replication occurs entirely outside the organism itself, through the assemblage of external source material.

This process has been known at the molecular level, where molecules (like proteins) can guide the assemblage of identical molecules using external resources. However, this process is entirely unknown at the cellular level or above.

In the case of xenobots, the researchers placed them in an environment with lots of individual stem cells. The xenobots spontaneously gathered these stem cells into copies of themselves. However, these copies were not able to replicate themselves, so the process ended after one or a very limited number of generations. In a new study, the researchers set out to design an optimal xenobot that could sustain many generations of self-replication. They did not do this the old-fashioned way, through extensive trial and error. Rather, they used an AI simulation, which calculated for literally months, testing billions of possible configurations. It came up with a simple shape – a sphere with a mouth, looking incredibly like a Pac-Man. These xenobots are comprised of about 3,000 cells. The researchers assembled their xenobot Pac-Men and when placed in an environment with available stem cells they spontaneously herded them into spheres and then into copies of themselves. These copies were also able to make more copies of themselves, and so-on for many generations.

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There are different ways of looking at history. The traditional way, the one most of us were likely taught in school, is mostly as a sequence of events focusing on the state level – world leaders, their political battles, and their wars with each other. This focus, however, can be shifted in many ways. It can be shifted horizontally to focus on different aspects of history, such as cultural or scientific. It can also zoom in or out to different levels of detail. I find especially fascinating those takes on history that zoom all the way out, take the biggest perspective possible and look for general trends.

A recent study does just that, looking at societal factors that drive the development of military technology in pre-industrial societies, covering a span of 10,000 years. They chose military technology for two main reasons. The first is simply convenience – military technology is particularly well preserved in historical records. The second is that military technology is a good marker for overall technology in most societies, and tends to drive other technologies. This remains true today, as cutting edge military technology (think GPS) often trickles down to the civilian world.

As an interesting aside, the researchers relied on Seshat: Global History Databank. This is a massive databank of 200,000 entries on 500 societies over 10,000 years. This kind of data is necessary to do this level of research efficiently, and is a good demonstration of this more general trend in scientific research – in increasing areas of research, it’s all about big data.

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Syukuro Manabe, Klaus Hasselmann, and Giorgio Parisi share this year’s Nobel Prize in Physics for their work increasing our understanding of how complex systems work. This is a powerful tool for understanding the world, which reminds me of previous advances in our understanding of how gases behave.

Gases are a phase of matter in which high energy particles are bouncing around at random. It would be impossible to predict the pathway of any individual gas molecule. However, collectively all of this random complexity follows very predictable laws. Similarly, weather is a very complex system. We can predict weather that is about to happen, but beyond a few days it becomes increasingly difficult. The system is simply too chaotic. However, climate (long term weather trends) follows theoretically predictable patterns. The trick is to see the hidden patterns in the chaos, and that is the work that these three physicists did.

Manabe and Hasselmann share half the prize for their work on climate models:

Syukuro Manabe demonstrated how increased levels of carbon dioxide in the atmosphere lead to increased temperatures at the surface of the Earth. In the 1960s, he led the development of physical models of the Earth’s climate and was the first person to explore the interaction between radiation balance and the vertical transport of air masses. His work laid the foundation for the development of current climate models.

About ten years later, Klaus Hasselmann created a model that links together weather and climate, thus answering the question of why climate models can be reliable despite weather being changeable and chaotic. He also developed methods for identifying specific signals, fingerprints, that both natural phenomena and human activities imprint in the climate. His methods have been used to prove that the increased temperature in the atmosphere is due to human emissions of carbon dioxide.

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It is not uncommon, if you do not like any particular finding of scientific research, to attack the institutions of science or even the very notion of science itself. These kinds of attacks are now common in the anti-vaccine pushback against common sense public health measures, and often from a religious or ideological perspective. It’s not surprising that the false claim that science is just philosophy has reared its head in such writings. The attack on science also tends to have at least two components. The first is a straw man about how scientists are pretending that science is a monolithic perfect and objective entity. This is then followed by the claim that, rather, science is just opinion, another form of subjective philosophy. This position is entirely wrong on both counts.

Here is one example, embedded in a long article loaded with misinformation about vaccines and the COVID pandemic. There is way too much misdirection in this article to tackle in one response, and I only want to focus on the philosophical claims. These are now common within certain religious circles, mostly innovated, at least recently, in the fight against the teaching of evolution. They have already lost this fight, philosophically, scientifically, and (perhaps most importantly) legally, but of course that does not mean they will abandon a bad argument just because its wrong.

First the straw man:

The second consequence of “following science” is that it reinforces one of modernity’s most enduring myths: that “science” is a consistent, compact, institutionally-guaranteed body of knowledge without interest or agenda. What this myth conceals is the actual operation of the sciences—multiple, messy, contingent, and tentative as they necessarily are.

The myth is itself a myth. It exists almost nowhere except in the minds of science deniers and those with an anti-science agenda. Elsewhere the author admits:

As a lay person, unqualified to judge the technical issues, I have concluded only that there might be a legitimate question here, and one that must, necessarily, remain open until time and experience can settle it.

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The ultimate goal of scientific skepticism is to skillfully use a process that has the maximal probability of accepting claims that are actually true and rejecting those that are false, while suspending judgment when an answer is not available. This is an open-ended process and is never complete, although some conclusions are so solid that questioning them further requires an extremely high bar of evidence. There are many components to scientific skepticism, broadly contained within scientific literacy, critical thinking skills, and media savvy. Traditional science communication focuses on scientific literacy (the so-called knowledge deficit model), but in the last few decades there has been copious research showing that this approach is not only not sufficient when dealing with many false beliefs, it may even be counterproductive.

A new study offer more evidence to support this view, highlighting the need to combine scientific literacy with critical thinking in order to combat misinformation and false claims. The study focuses on the effect of trust in science as an independent variable, and combined with the ability to critically evaluate scientific evidence. In a series of four experiments they looked at acceptance of false claims regarding either a fictional virus, or false claims about GMOs and tumors:

Depending on experimental condition, however, the claims contained references to either (a) scientific concepts and scientists who claimed to have conducted research on the virus or GMOs (scientific content), or (b) lay descriptions of the same issues from activist sources (no scientific content).

They wanted to see the effect of citing scientists and research on the acceptance of the false claims. As predicted, referring to science or scientists increased acceptance. They found that subjects who scored higher in terms of trust in science were more likely to believe false claims when scientists were cited – so trust in science made them more vulnerable to pseudoscience. For those with low trust in science, the presence or absence of scientific content had no effect on their belief in the false claims. These results replicated in the first three studies, using the fictional virus and the GMO claims.

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We are approaching two years into this pandemic and we still haven’t proved the origins of the SARS-CoV-2 virus. However, this is not unusual at all, and in itself is not suspicious. It took 13 years to identify the origin of SARS, and we have never identified the origins of some Ebola outbreaks. But what do we know about the origins of SARS-CoV-2? The question has become highly political, which is unfortunate. Let’s review what the actual evidence has to say.

If we go back to the beginning of the pandemic, the early scientific investigation of the virus found that it was 96% identical to a bat coronavirus in the region. Zoonotic spillover is common, and the virus originated in a part of the world with wet markets and close contact with wild animal populations. Direct examination of the virus also did not show any telltale signs of deliberate manipulation. There has been some scientific debate on this topic, but in the end there is general agreement among scientists that there is no smoking gun of genetic manipulation. For these reasons it was concluded early that the most probable origin of COVID was from animals, either directly from bat to human or through an intermediary.

This conclusion was based on examination of the virus itself and the the reservoirs of similar viruses in the region. This was, and by many still is, considered the most likely origin. Researchers have searched for the precise animal origin, and so far have not found it, but that is not in itself unusual or suspicious.

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