Science In My Fiction

When I was a kid, dresses weren’t the problem. I was. Of all the sticks and stones lobbed in my direction, ‘tomboy’ was one of the kindest. I didn’t help my circumstances by refusing to wear pink or pigtails or shoes that went ‘click’ on the sidewalk.

I wasn’t just a no-frills kind of girl. On school picture day, I rocked a pair of  boys’ Transformers sandals. There was more to me than met the eye. True, I was born with certain genitals and I wore my hair very, very long until I was an adult. But no matter how hard people tried – and sometimes they tried with fists and guns – nobody was able to convince me that my crotch defined my self.

Girl or boy, gender was an imposition as far as I was concerned. I took to it like I took to a beating: With my guard up and my head down. That is, until I grew up enough to ‘fight like a man’. After that, I started hearing a lot of, “Babe, you have to let the boys win.” Why? “Because if you don’t, some guy’s gonna kill you.”

Those were the stakes. Be a proper girly-girl. Accept your role. Take it. Or else.

Pardon me while I carry on answering that threat of violence with a rude gesture of my own.

Ordinary people say a lot of daft things:

• Gender and sex are the same thing.
• Gender is innate and never changes (or should never change).
• Gender determines sexuality (and it should).
• I’m/she’s a girl, so I/she naturally [fills in the blank like a girl].
• I’m/he’s a boy, so I/he naturally [fills in the blank like a boy].

When called out for telling lies and otherwise embarrassing themselves, they raise the usual defenses:

• I can’t help it; I was brought up this way.
• God says [whatever I say].
• Science says—

GOTCHA! Science says that all humans are far more alike than we are different from each other, regardless of gender, sex, sexuality, race, or [you-name-it]. In unbiased experiments, the binary sexes (female/male) are effectively indistinguishable from each other. There isn’t a lot of research done which includes the entire plurality of gender (or the many sexes), but given that most people fail to even recognize more than two genders, my educated guess is that science wouldn’t be able to find a significant difference between straight, white, cis-gendered men and asexual, multi-racial, intersex androgynous people. Because there is nothing to find except IDIC.

Writers are human, though, so they sometimes make this noise:

• My story’s not about that.
• My characters just formed [white/straight/]cis-gendered.
• I write for kids, and this ‘subject matter’ is too mature.
• This is historical fiction, and gender wasn’t a ‘thing’ in the past.

To which I must answer:

• Maybe not, but while opportunity is leaning on the doorbell, you’re hiding under the bed.
• Who’s in charge, here? You, or the figments of your imagination?
• Bullshit. Kids are swimming in this ‘subject matter’ while you’re refusing to write them something potentially life-saving.
• BWAHAHAHAHA! (Do better research.)

These are usually met with hand-wringing and sham-sincerity: “I’m afraid of screwing it up. I don’t want to offend anyone.”

Tough luck, Pinocchio, because, first of all, there is such a thing as offense by omission. Secondly, you’re better off telling the truth: You can’t handle critique, and you don’t want to learn. Finally, if your writing never challenges convention or tradition, it’s probably not important. Deal with that.

This sort of careless writing and non-thinking is why science fiction and fantasy fans can’t have nice things, like a woman Doctor Who. And why the first book in a certain bestselling series wasn’t a stand-alone titled Hermione Granger Kills The Dark Lord With Her Brain. And why writers are still falling over themselves trying to write the next Twilight, of all crap.

Because when we reach for a hero, we keep reaching until we find a dude, and when we need a victim or a dummy, we grab a chick (and put her in the fridge). Those characters who don’t fit the cis-gender binary are ignored completely… Until somebody needs a truly sinister villain. Or a corpse. Then it’s like a pride parade breaks out on the page.

Fortunately, there are some quick and easy shortcuts to avoid being a gender jerk in fiction:

I lied; there are no shortcuts. Educate yourself. Read stories you’re too timid to write. Read blog posts and articles by people whose very identities challenge your notions about what is ‘normal’ and ‘right’. Get uncomfortable. Spend some quality time with a mirror and a microscope. If you examine yourself honestly and find nothing about who you are that’s unconventional, please cast your likeness as the villain in your next story.

You might win an award for giving everybody the creeps.

Recommended reading:

“Baggage Check” by Shay Darrach

FINE a comic by Rhea Ewing

Anita Sarkeesian’s Feminist Frequency

I recently read Ferrett Steinmetz‘s short story “Sourdough Station” that as the title suggests involves a sauerkraut-making operation on a space station. That’s not all the story is about, of course, but it did get me thinking about food and fermentation and what that might mean to folks living in space.

Sauerkraut is fermented cabbage and is high in vitamins, fiber, iron, folate and other nutrients.  Combine that with sauerkraut’s relatively long shelf life, and it seems like an ideal food for isolated outposts in space. Astronauts have already been experimenting with growing nappa cabbage and vegetables on the International Space Station, so it looks likely that growing leafy greens in space is within the realm of possibility. Assuming that the fermentation process works as well in space as it does on Earth, sauerkraut could become a space habitat staple as humans spread out among the stars. Or perhaps spacegoers would prefer something more like spicy Korean kimchi, since microgravity can dull the sense of taste.

Lactic acid bacteria  – primarily species of Lactobacillus – are not only involved in the process of fermenting sauerkraut and kimchi, but also the production of yogurt and cheese from milk, and making sourdough bread sour. But not any old microbe will do for optimal fermentation. Different species of bacteria are used in the production of different foods: Lactobacillus kimchii is a unique species found (naturally) in kimchi, Lactobacillus helveticus is used to ferment milk into swiss cheese, Lactobacillus delbrueckii subsp. bulgaricus (discovered in the course of researching the longevity of Bulgarians) and Lactobacillus acidophilus are used in yogurt production, and Lactobacillus sanfranciscensis gives sourdough bread its sourness.

The different species and strains of bacteria vary in their biochemistry and so can significantly affect the flavor of the sauerkraut or yogurt or bread being produced. Often there are several different species of bacteria that are involved in the fermentation process. DNA analysis during the sauerkraut production process has found a number of different bacterial species present during the fermentation process. There may be an entire ecosystem of microbes in every fermentation pot.

So why is this important to my hypothetical sauerkraut-eating spacefarers of the future?

Even assuming there are no technical issues with designing safe fermentation vessels or growing vegetables to ferment, culturing the necessary microbes might turn out to be a challenge.

Even under optimal conditions of temperature and humidity, space stations are unlikely to have gravity equal to that on Earth and that can affect bacterial growth. For example,  Lactobacillus acidophilus has been shown to grow more quickly in the microgravity environment of the International Space Station.

It’s not a stretch to wonder whether new strains of bacteria will have to be developed – or perhaps will arise naturally – for the production of deliciously fermented food in space. It wouldn’t be that far different from the development of new strains of yeast that revolutionized the brewing industry here on Earth.

But the fact that the background radiation levels on a space station or spaceship could be significantly higher than that on Earth could significantly raise the mutation rate in bacteria onboard, and there is always a risk that such mutations could render otherwise harmless bacteria dangerous. And even harmless bacteria could harbor mutations that modify their metabolism in such a way that it affects the fermentation process or the flavor of the fermented product.

At the turn of the 20th century Alaskan gold rush old-timers were known as Sourdoughs because they were reputed to protect their sourdough cultures during Arctic winters by keeping lumps of dough warm with their bodies. Spacefarers would similarly have to carefully protect and maintain any bacterial cultures used in food production.

I can imagine humans spread through our solar system and beyond, with different space colonies developing their own special fermentation cultures. Freeze-dried microbes would be easy to carry and trade, perhaps helping form the basis of a space culture barter system. They could be known for this, perhaps becoming the Sourdoughs of space*. They probably wouldn’t be so grizzled (or as nearly exclusively male) as the Yukon prospectors of a century ago, but like the original Sourdoughs would be living in an environment hostile to humans and they would known for the products of those precious microbes they maintained.

Since food plays such an important role in human culture, I like to think that’s how we’ll refer to ourselves.

Or maybe I’m just hungry …

Top image: Fermented foods made with lactic acid producing bacteria. From “Genomic comparison of lactic acid  bacterial published“, DOE Joint Genome Institute.

Bottom image: Lactobacillus casei uploaded by AJ Cann (AJC1) on Flickr and shared under a CC BY-SA 2.0 license.

Science fiction is full of examples of artificial minds. From Robbie the Robot from Forbidden Planet and Hal, from 2001, (technically, just a computer) all the way to Battlestar Galactica’s Cylons including the hideous (not!) Caprica 6. One of the best known types of robots are the ones imagined by Isaac Asimov. He was one of the first writers that in addition of creating robot characters, also incorporated in many of his stories a mechanism for creating artificial intelligence. He did this by “inventing” a still fictional technology for creating something called positronic brains. We will not go over positronic brains here though; maybe at some other time.

Regardless of the actual physical form that an artificial intelligence may have, the important thing (from the perspective of this post) is that some kind of mind/intelligence is at play. An implicit question is immediately apparent. How complex does a physical system has to be for the emergence of a human-style mind? In other words, what is the minimal number of interacting neurons that we need for consciousness to appear?

When we humans think about numbers, especially if we need to count a big amount of anything, we tend to think in groups of ten. This is most likely a consequence of (usually) having ten fingers. Because of that, people like working with “nice round numbers” usually some power of 10.

What are these “powers of 10”?

Well, you know: 10, 100, 1000, 10000, etc. We also use expressions like “order of magnitude”, which means essentially the same thing. This is, when something differs from anything else by (usually) a factor of ten, we are talking about an order of magnitude. Similarly, two orders of magnitude is a factor of 100 and so on. We just happen to feel comfortable thinking in those terms.

Now, when thinking about nature with that frame of mind, we come across to a lot of very curious coincidences. For example, the best available data suggests that there are close to 100 (maybe 200) billion galaxies in the known universe, each of them with an average of 100 billion stars. A billion, by the way, is a very big number. To avoid any confusion, what we mean by a billion here is 1,000,000,000, and it is a big number indeed. Just to give you some perspective, if you were to count to a billion at roughly one number per second, nonstop, you would need about 32 years.

Yep.

Another famous “100 billion” amount is the estimated number of nerve cells in a typical human brain. Oddly, nobody knows exactly where this estimate came from, even though is THE figure cited in virtually every book, every newspiece, etc. Because of that, a research group stepped up to the plate and decided to do the experiments to actually try and count the neurons in a human brain.

The result of that was the following paper:

Azevedo FA, Carvalho LR, Grinberg LT, Farfel JM, Ferretti RE, Leite RE, Jacob Filho W, Lent R, Herculano-Houzel S (2009) Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain. J Comp Neurol. 513(5):532-41.

The leader of this group is an accomplished neuroscientist, Dr. Suzana Herculano-Houzel, of the Federal University of Rio de Janeiro, Brazil. She kindly sent me a copy of her paper as well as many other papers on her research about the evolution of the brain. The paper is very well-written and their conclusions are logical and sound. However, these results triggered a minor controversy regarding the exact number of neurons in a human brain. The controversy was in no way the fault of any of the researchers; rather, I blame the current editorial trend of “byte-size” science that merely throws a flashy headline to get attention. Nothing wrong with that, mind you, except when the headline is not followed by adequate reporting. When this follow up does not happen, it will create deep misunderstandings.

Not so long ago, James Randerson wrote a piece for “The Guardian” titled: “How many neurons make a human brain? Billions fewer than we thought”, based on the Azevedo paper and an interview with Dr. Herculano-Houzel. Randerson’s piece is found here:

Basically, this piece “reports” that the Azevedo paper mainly found that the average number of neurons in humans is close to 86 billion. From the perspective of just this bare fact, yes indeed, we seem to be missing 14 billion neurons. Don’t panic though!

Azevedo’s paper describes how the measurements were done by determining the number of neurons by an interesting cell counting method that I will not discuss here (but it has been described as “brain soup” just in case you are interested in finding out more).

I want to clarify that I am not criticizing the paper. In fact, if find it quite interesting, that is why I read it!

But, back to topic…

There are several factors that immediately come to mind that need to be considered before using this sole paper as a reason to begin rewriting the textbooks to read “The human brain is composed of roughly 86 billion neurons…”

**Brains tend to shrink with age; moreover, the rate of shrinking is surely affected by genetics and environmental factors.

**The sample size was only 4 brains, all of them men’s with an age range from 50 to 71 years old.

**Gender.

**General health and levels of physical activity.

And we could keep going, but you basically get the idea, namely that as it is right now, this data is not sufficient to state that the 86 billion figure is the definitive one.

Other scientists have commented on these issues and have urged a more cautious approach. One of these scientists is a collaborator of the Herculano-Houzel group, Dr. Roberto Lent, also a coauthor of the paper. For more information on Dr. Lent’s point of view go here.

Additional details:

The Azevedo paper reports finding and average of 86.1 +\- 8.1 billion neurons. This means that the actual number of neurons (the approximate range) can be as little as 78 billion or as high as 94.2 billion give or take (perilously close to the 100 billion accepted figure, gasp!). Now, the +\- represents the standard deviation, and the results seem to be reliable.

Thus, in my humble opinion, the number of neurons per brain reported in the paper does not seem to be significantly different from 100 billion as a first approximation. To the author’s credit, not a big deal is made of the issue of the number of cells in the paper. Furthermore, the main subject of the paper, explicitly stated in its text (and even in the title) is not neurons, but glial cells, namely that it seems that on average, there is one glial cell per neuron. Now, to me that is the most interesting thing about the paper. Why?

When they were originally discovered, glial cells were thought to play just a structural or supporting role in the nervous system; in fact, the very word glia means “glue”. However, glial cells are much more than that; we now know that they are quite the active partners in brain physiology. They work together with neurons so there is little doubt that glial cells affect many aspects of nervous system function. I am sure that there will be more interesting discoveries down the road!

As I said, I do not hold the authors of the paper responsible for this confusion. Rather, I feel kind of annoyed when I read reports (like the one in The Guardian) that pretend to be factual without the complete, relevant information required to reach a proper series of conclusions. It may well be that the 86 billion figure is significant and it tells us something about our brain or it may as well not, but we will not know which one it is with incomplete information; that”s for sure. Already, several prominent science writers have begun to talk about the 86 billion neurons in human brains as a fact. This is quite premature and a disservice to the public and to science writing as well.

That’s why I tend to mistrust byte-size, headlines-driven science reporting. Science writers have a very serious mission.

It is not that the exact number of neurons that we may have is unimportant, not at all! This piece of information will undoubtedly help us when designing true artificial intelligence, namely by saying, how many neurons do we have? Then again, the actual number may not be as important as how all those cells communicate with each other through chemical and electrical transmission, but that’s a yet another topic for some other time.

The take-home message: Take science-related hyperbolic news with a grain of salt, and whenever possible, go straight to the source to find out what the main point was (it kind of rhymes… ).

In the meantime, I will keep using my neurons and try not to think about whether I have 80 or 100 billion of them.

Picture credit: http://1.bp.blogspot.com/

This post is based on two posts previously written for my Baldscientist blog, with some updates added and explicitly linking it to science fiction. You can find the original posts here and here.

****Already an update! I contacted Dr. Roberto Lent and he informed me that a follow-up paper with more brains is about to be published! Stay tuned!****

If you want to know more:

Azevedo FA et al., (2009) Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain. J Comp Neurol 513(5):532-41.

Fields RD (2010) The Other Brain. Simon and Schuster.

Fields RD (2004) The Other Half of the Brain. Scientific American 290:54-61.

“Probability is the bane of the age,” said Moreland, now warming up. “Every Tom, Dick, and Harry thinks he knows what is probable. The fact is most people have not the smallest idea what is going on round them. Their conclusions about life are based on utterly irrelevant – and usually inaccurate – premises.”

Anthony Powell, “Casanova’s Chinese Restaurant” in 2nd Movement in A Dance to the Music of Time, University of Chicago Press, 1995

Because many events can’t be predicted with total certainty, often the best we can do is say what the probability is that an event will occur – that is, how likely it is to happen. The probability that a particular event (or set of events) will occur is expressed on a linear scale from 0 (impossibility) to 1 (certainty), or as a percentage between 0 and 100%.

The analysis of events governed by probability is called statistics, a branch of mathematics that studies the possible outcomes of given events together with their relative likelihoods and distributions. It is one of the last major areas of mathematics to be developed, with its beginnings usually dated to correspondence between the mathematicians Blaise Pascal and Pierre de Fermat in the 1650′s concerning certain problems that arose from gambling.

Chevalier de Méré, a French nobleman with an interest in gaming and gambling questions, called Pascal’s attention to an apparent contradiction concerning a popular dice game that consisted in throwing a pair of dice 24 times. The problem was to decide whether or not to bet even money on the occurrence of at least one “double six” during the 24 throws. A seemingly well-established gambling rule led de Méré to believe that betting on a double six in 24 throws would be profitable, but his own calculations indicated just the opposite. This problem (as well as others posed by de Méré) led to the correspondence in which the fundamental principles of probability theory were formulated for the first time.

Statistics is routinely used in in every social and natural science. It is making inroads in law and in the humanities. It has been so successful as a discipline that most research is not regarded as legitimate without it. It’s also used in a wide variety of practical tasks. Physicians rely on computer programs that use probabilistic methods to interpret the results of some medical tests. Construction workers use a chart based on probability theory when mixing the concrete for the foundation of buildings, and tax assessors use a statistical package to decide how much the house is worth.

While there a number of forms of statistical analysis, the two dominant forms are Frequentist and Bayesian.

Bayesian analysis is the older form, and focuses on P(H|D) – the probability (P) of the hypothesis (H), given the data (D). This approach treats the data as fixed (these are the only data you have) and hypotheses as random (the hypothesis might be true or false, with some probability between 0 and 1). This approach is called Bayesian because it uses Bayes’ Theorem to calculate P(H|D).

The conceptual framework for Bayes’ Theorem was developed by the Reverend Thomas Bayes), and published posthumously in 1764. It was perfected and advanced by French physicist Pierre Simon Laplace, who gave it its modern mathematical form and scientific application. Bayes’ theorem has a 250-year history, and the method of inverse probability that was developed from it dominated statistical thinking into the twentieth century.

For the Bayesian:
• Probability is subjective – a measurement of the degree of belief that an event will occur – and can be applied to single events based on degree of confidence or beliefs. For example, Bayesian can refer to tomorrow’s weather as having a 50% chance of rain.
• Parameters are random variables that have a given distribution, and other probability statements can be made about them.
• Probability has a distribution over the parameters, and point estimates are usually done by either taking the mode or the mean of the distribution.

A Bayesian basically says, “I don’t know how the world is. All I have to go on is finite data. So I’ll use statistics to infer something from those data about how probable different possible states of the world are.”

Frequentist (sometimes called “a posteriori”, “empirical”, or “classical”) analysis focuses on P(D|H), the probability (P) of the data (D), given the hypothesis (H). That is, this approach treats data as random (if you repeated the study, the data might come out differently), and hypotheses as fixed (the hypothesis is either true or false, and so has a probability of either 1 or 0, you just don’t know for sure which it is). This approach is called frequentist because it’s concerned with the frequency with which one expects to observe the data, given some hypothesis about the world.

Frequentist statistical analysis is associated with Sir Ronald Fisher (who created the null hypothesis and p-values as evidence against the null), Jerzy Neyman (who was the first to introduce the modern concept of a confidence interval in hypothesis testing) and Egon Pearson (who with Neyman developed the concept of Type I and II errors, power, alternative hypotheses, and deciding to reject or not reject based on an alpha level). They use the relative frequency concept – you must perform one experiment lots of times and measure the proportion where you get a positive result.

For the Frequentist:
• Probability is objective and refers to the limit of an event’s relative frequency in a large number of trials. For example, a coin with a 50% probability of heads will turn up heads 50% of the time.
• Parameters are all fixed and unknown constants.
• Any statistical process only has interpretations based on limited frequencies. For example, a 95% confidence interval of a given parameter will contain the true value of the parameter 95% of the time.
• Referring to tomorrow’s weather as having a 50% chance of rain would not make sense to a Frequentist because tomorrow is just one unique event, and cannot be referred to as a relative frequency in a large number of trials. But they could say that 70% of days in April are rainy in Seattle.

A Frequentist basically says, “The world is a certain way, but I don’t know how it is. Further, I can’t necessarily tell how the world is just by collecting data, because data are always finite and noisy. So I’ll use statistics to line up the alternative possibilities, and see which ones the data more or less rule out.”

Frequentist and Bayesian approaches represent deeply conflicting approaches with deeply conflicting goals. Perhaps the most important conflict has to do with alternative interpretations of what “probability” means. These alternative interpretations arise because it often doesn’t make sense to talk about possible states of the world. For instance, there’s either life on Mars, or there’s not.

We don’t know for sure which it is, but we can say with certainty that it’s one or the other. So if you insist on putting a number on the probability of life on Mars (i.e. the probability that the hypothesis “There is life on Mars” is true), you are forced to drop the Frequentist interpretation of probability. A Frequentist interprets the word “probability” as meaning “the frequency with which something would happen in a lengthy series of trials”.

The Bayesian interprets the word “probability” as “subjective degree of belief” – the probability that you (personally) attach to a hypothesis is a measure of how strongly you (personally) believe that hypothesis. So a Frequentist would never say “There’s probably not life on Mars”, unless they were speaking loosely and using that phrase as shorthand for “The data are inconsistent with the hypothesis of life on Mars”. But the Bayesian would say “There’s probably not life on Mars”, not as a loose way of speaking about Mars, but as a very literal and precise way of speaking about their beliefs about Mars. A lot of the choice between Frequentist and Bayesian statistics comes down to whether you think science should comprise statements about the world, or statements about our beliefs.

Let’s look at the simple task of flipping a coin. The flip of a fair coin has no memory, or as mathematicians would say, each flip is independent. Even if by chance the coin comes up heads ten times in a row, the probability of getting heads or tails on the next flip is precisely equal. You may believe that a coin that, because a flipped coin has come up heads ten times in a row, that “tails is way overdue”, but the coin doesn’t know and doesn’t care about the last ten flips; the next flip is just as likely to be the eleventh head in a row as the tail that breaks the streak. The probability that the flip of a fair coin will come up heads or tails, then, is 50%.

But what, exactly, do we mean when we say that the probability is 50%? A Frequentist would say that if the probability of landing or either side is 50%, this means that if we were to repeat the experiment of flipping the coin a large number of times, we would expect to see approximately the same number of heads as tails. That is, the ratio of heads to tails will approach 1:1 as we flip the coin more and more times.

In contrast, a Bayesian would say that probability is a very personal opinion. What probability of 50% means to you is different from what it might mean to me. If pressed to place a bet on the outcome of flipping a single coin, you would just as well guess heads or tails. More generally, if you were to bet on the flip of a coin and was told that the probability of either side coming up was 50%, and the rewards for guessing correctly on any outcome are equal, then it would make no difference to you what side of the coin you bet on.

Both approaches are addressing the same fundamental problem (what are the odds that flipping a coin will result in it landing heads up), but attack the problem in reverse orders (the probability of getting data, given a model, versus probability of a model, given some data). It’s quite common to get the same basic result out of both methods, but many will argue that the Bayesian approach more closely relates to the fundamental problem in science (we have some data, and we want to infer the most likely truth.)

So, which approach is best? The Frequentist position would seem to be the answer. In our coin-flipping example, the probability of a fair coin landing heads is 50% because it lands heads half the time. Defining probability in terms of frequency seems to be the empirical thing to do. After all, frequency is “real”. It isn’t metaphysical, like “degree of certainty,” or “degree of warranted belief.” You can go out and observe it.

However, the Frequentist position also has some significant problems. First, it requires the long run relative frequency interpretation of probability – that is, the limiting frequency with which that outcome appears in a long series of similar events. Dice, coins and shuffled playing cards can be used to generate random variables; therefore, they have a frequency distribution, and the frequency definition of probability theory can be used. Unfortunately, the frequency interpretation can only be used in cases such as these. Another problem is that almost all prior information is ignored, and it doesn’t allow you to incorporate what you already know. Even more seriously, a hypothesis that may be true may be rejected because it hasn’t predicted observable results that have not occurred.

But the Bayesian position has its own set of problems. Bayesian calculations almost invariably require integrations over uncertain parameters, making them computationally difficult. Second, Bayesian methods require specifying prior probability distributions, which are often themselves unknown. Bayesian analyses generally assume so-called “uninformative” (often uniform) priors in such cases. But such assumptions may or may not be valid, and more importantly, it may not be possible to determine their validity with any degree of certainty.

Finally, though Bayes’ theorem is trivially true for random variables X and Y, it’s not clear that parameters or hypotheses should be treated as random variables. It’s accepted that you can talk about the probability of observed data given a model – the frequency with which you would obtain those data in the limit of infinite trials. But if you talk about the “probability”’ of a one-time, non-repeatable event that is either true or false, there is no frequency interpretation.

While both approaches have their (often rabid) proponents, I would argue that the approach you take depends on the question (or questions) you’re asking. Let’s take the hypothetical case of a patient you want to perform a test on.

You know the patient is either healthy (H) or sick (S). Once you perform the test, the result will either be Positive (+) or Negative (-). Now, let’s assume that if the patient is sick, they will always get a Positive result. We’ll call this the correct (C) result and say that if the patient is healthy, the test will be negative 95% of the time, but there will be some false positives. In other words, the probability of the test being Correct, for healthy people, is 95%. So the test is either 100% accurate or 95% accurate, depending on whether the patient is healthy or sick. Taken together, this means the test is at least 95% accurate.

These are the statements that would be made by a Frequentist. The statements are simple to understand and are demonstrably true. But what if we ask a more difficult, and arguably a more useful question – given the test result, what can you learn about the health of the patient?

If you get a negative test result, the patient is obviously healthy, as there are no false negatives. But what if the test is positive? Was the test positive because the patient was actually sick, or was it a false positive? This is where the frequentist and Bayesian diverge. Everybody will agree that this cannot be answered at the moment. The frequentist will refuse to answer. The Bayesian will be prepared to give you an answer, but you’ll have to give the Bayesian a prior first – i.e. tell it what proportion of the patients are sick.

If you are satisfied with statements such as “for healthy patients, the test is very accurate” and “for sick patients, the test is very accurate”, the Frequentist approach is best. But for the question “for those patients that got a positive test result, how accurate is the test?”, a Bayesian approach is required.

References

Ambaum, Maarten H. P., 2012. Frequentist vs Bayesian statistics—a non-statisticians view. http://arxiv.org/abs/1208.2141

Bayarri, M.J. and Berge, J.O. The Interplay of Bayesian and Frequentist Analysis. Statist. Sci. Volume 19, Number 1 (2004), 58-80.

Fienberg, Stephen E., 2006. When Did Bayesian Inference Become Bayesian? Bayesian Analysis Volume 1, Number 1, pp. 1-40.

Gustafson, Paul and Greenland, Sander, 2009. Interval Estimation for Messy Observational Data. Statist. Sci. Volume 24, Number 3, 28–342.

Hald, Anders, 2003. A History of Probability and Statistics and Their Applications before 1750. Hoboken, NJ: Wiley-Interscience

Hampel, Frank, 1998. On the foundations of statistics: A frequentist approach, Research Report No. 85. Zurich, Switzerland: Seminar fur Statistik, Eidgenossische Technische Hochschule (ETH)

Samaniego, Francisco J., 2010. A Comparison of the Bayesian and Frequentist Approaches to Estimation. New York, NY: Springer

Shafer, Glenn, 1990. The Unity and Diversity of Probability. Statist. Sci. Volume 5, Number 4, 435-444.

Zabell , Sandy , 1989. R. A. Fisher on the History of Inverse Probability. Statist. Sci. Volume 4, Number 3, 247-256.

Last weekend I got lost twice, once going to and then coming from a local bookstore that I’ve been to several times. I only travel out of town in cases of rare necessity. My ability to get lost defies the assistance of MapQuest and the like.  The timing of my adventure couldn’t be better. It was the event that tipped the scales in favor of rats vs. the promised glowing fish from my last article.

I knew as soon as I saw the link to “A Sense of Where You Are” on Jay Lake ‘s Link Salad, that I had to explore the topic here. The article discusses two doctors, May-Britt and Evard Moser. The husband and wife team direct the Kavli Institute for Systems Neuroscience and Centre for the Biology of Memory  which is a neuroscience research center at the Norwegian University of Science and Technology. Under their direction, the center has become known for the discovery of grid cells. The cells help rats know where they are, remember where they’ve been, and understand where they are going.

“The scientific goal of the Kavli Institute for Systems Neuroscience is to advance our understanding of neural circuits and systems. By focusing on spatial representation and memory, the investigators hope to uncover general principles of neural network computation in the mammalian cortex.” (Wikipedia Kavli stub article)

My husband is very good at finding his way around our city and reaching destinations without getting lost. My parents are too. In fact, I couldn’t remember a single time that we got lost on road trips as a kid. I decided to call my mom and fact check my memory against her’s.

Mom told me that Carol is more like me. She has difficulty placing the city, county, and state on the map in her mind without one in front of her. I just hadn’t known Mom was her co-pilot for our road trips back when we were kids. Mom then reminded me of Kittery, Maine.

We’d been house hunting in the great state of Maine, and Kittery was one of few towns that a motel that allowed you to take dogs inside. We always stayed there a few times while going up and down the state. When Carol and I would go out foraging for local take out, we got lost. Often. I remember crossing a bridge that meant you were leaving Maine and going into New Hampshire. The roads were confusing to us, and somehow we kept making the same mistakes. It was scary at first but funny after a while. it turned into Groundhog Day – the you aren’t from around here version. I had a good laugh when Mom brought up the movie. That is exactly how it felt.

It got me thinking about how Bill Murray’s character learned the layout of town and the timing of the events of the day. Rats in these experiments become familiar with their mazes and will often return to where they found or stored food even with their sense of smell taken out of the equation. (Researchers apparently put food under the maze to mask where it might be in the maze.) It isn’t just rats; squirrels and birds also remember where they put their food. I have no link for the squirrels. I have a friend that could attest to this: something about flower pots and peanut shells. I am not sure I recommend that you ask her, though.

Rats also know how to get out of a flood. I’ve seen it in movies and read it in books countless times. In times of disaster, you follow the rats out. There is a test that demonstrates this, Morris water navigation task.

In reading about Spatial memory I began to understand some of the reasons behind my uncanny ability to get lost anywhere. The added layers of sensory input, echoes of previous trips that went to nearby locations (which blur landmarks, boundaries  and my  association to them), the distracting traffic and receiving imput from a passenger who is a little challenged herself with this at times (the overlay of routes she’s taken with others does this to her) all work together to confuse the Grid cells that create my Cognitive map. At least, this is the theory I am going with after the research I’ve been doing for this article.

Maybe with my brain sorting through data in unorganized, rapid succession I get gridlocked. No, that isn’t a scientific term, but maybe it is coming. Speaking of gridlock, some scientists have tested taxi cab drivers in virtual settings. Hello, The Matrix! They found out that these spacial memory experts are better at recall of this nature and worked to understand why. In another study, a virtual taxi driving game was used to further analyze this sense of direction while testing  to see if stimulation of this memory area in the hippocampus and the entorhinal cortex (EC), while learning, makes the memory stick. The entorhinal cortex is one of the first areas affected Alzheimer’s Disease causing patients to suffer the loss of spacial memory very early in the progression of the disease. This research, Memory Enhancement and Deep-Brain Stimulation of the Entorhinal Area, might tie into ways to counteract those effects.

Here are some random thoughts I had regarding this topic:

I wonder if my inability to accurately translate dance moves I see to ones produced by my body has any relation to my frequent adventures in getting lost. Spacial memory is also in charge of movement. Does this explain why I can’t dance?

Is the memorizing technique, Method of loci, more difficult to use for those of us with a poor sense of direction?

Are we more or less likely to notice when things are rearranged at home or work because it upsets our landmarks and boundaries that we rely so heavily upon even in these familiar places? I know it leaves me feeling off kilter.

Do grid cells, cognitive maps, and spacial memory tie into animal migration and navigation? Some animals are born with an understanding of where they should be headed when they get older. Elephants remember where their ancestors are buried. So are there genetic markers that lead the hippocampus to develop this understanding? How does this factor into their social learning?

Consider what might happen if scientist found a way to tinker with this in animals.  Imagine being able to create natural psychological boundaries for packs of wolves, the selling point being they would be kept off the farmland or ranches and kept in their sanctuaries. We have bears that keep coming into Greensboro. It is a little exciting and unexpected. Local wildlife authorities just tranquilized a bear that has been here twice now. He is tagged and they know this for certain. What if there was a way to steer him clear of this danger before someone decides he is just going to keep coming back and should be shot? Say a nanobot-sized thing could be put in the drinking water of a herd, and the nanobots could wind up in the brains of a herd of moose and steer them clear of say the busier highways or cities so that as they travelled they would face less peril.

In Stephen King’s Cell the not-zombies seem to flock and seem to migrate. I wonder if the heart of that fictionalized action could also be based in the hippocampus as the non-zombies still possess their motor skills.

We have GPS systems at our finger tips. Will this memory exercise become stale for us? Five generations out from now, will we only be able to travel with the aid of technology. Will we lose our sense of direction?

And just for fun because I enjoy this series and it is remotely related: Tattoo Typos, Senses, Posters and Bad Movies –  A Vlogbrothers YouTube Post

There’s no way that humans can head into space all by themselves. Even leaving aside the bacteria, fungi and arthropods that inhabit our bodies, and without which we won’t stay healthy for long, once we go in for large-scale space travel and exploration it’s going to be incredibly hard to keep insect pests and even small mammals from hitching a ride. (I don’t think any space programs so far have reported roaches or mice, but do you think they would?)

What about animals we intend to spend into space? Decades of animal research have resulted in dogs, cats, monkeys, chimps, and many, many lab mice in orbit. Also guinea pigs, frogs, fish, many species of insects… yes, even cockroaches, but only on purpose.

Those have all been for science, but science fiction at least has posited that people will want to have their familiar animal companions (or their science fictional equivalents).

We think that our pets are cute (and we might even be hard-wired to do so), but pets could also be good for us. There’s some evidence that animal companions can lower blood pressure, reduce anxiety, and improve immune system function. Those sound like good things, even in space (likely a stressful environment, don’t you think?).

Cats, ferrets and terriers would also be good at controlling the accidental animals, the rats and mice of our habitats. They’ve been bred for just that sort of work for centuries if not longer, as well as to live well in the company of humans. Or we could genetically engineer something new to fit our new lifestyles, instead of working through centuries of conventional breeding.

So what do you think? Would you take your pet into space, and why or why not? Will future creatures be just as cute as today’s pets? Will they have to work for a living, or is companionship enough?

… so little time.

There’s so much nifty science out there that our small group of writers is utterly overwhelmed, and that’s why we need you.

That’s right, you too could be part of the SiMF crew. We’re looking for people who love science and fiction both, regardless of their formal qualifications in either, love to write, and want to share those enthusiasms with the world. SiMF is an all-volunteer outfit: we do it for love, not money.

We’re most interested in short essays about how current science topics are relevant to speculative fiction, and we’re not particularly interested in reviews. An ideal writer will be able to contribute something every couple of months, but we’re also willing to consider one-off guest posts.

To apply, please email sarah.goslee at gmail dot com with a brief description of your qualifications and why you’re interested in SiMF, and either a link to relevant online articles you’ve written or a sample of your work that would be appropriate for SiMF.

A couple of weeks ago, the “offbeat news” feeds lit up with the discovery of a Japanese striped beakfish off the Washington state coast. What was surprising about this find was that the fish was in a bait box on a Japanese fishing boat believed to have drifted to its current position following the March 2011 tsunami in Japan.

This little guy laughs in the face of tsunamis. Creative Commons image, attributed to user E-190, retrieved via Wikimedia Commons.

The fish has, it seems, generated a lot of publicity for the Oregon aquarium now housing it. But it puts me in mind of another, even more unbelievable journey.

Our story starts around six hundred years ago with some Bavarian brewers of beer. Now, beer has been around since approximately a week longer than there have been humans, or at least humans who farmed barley¹. By the 15th century, in Europe², you could even say they’d gotten pretty good at it. Many of the styles of beer that we’re familiar with today were being produced³, and while yeast wasn’t well-understood by brewers until much later, they knew that adding the dregs of a good beer to newly-cooked wort would make the result similar, and these carefully-tended “starters” became the closely-guarded trade secrets of commercial and monastic breweries.

The good old days, where nobody minded if you stuck your hand in the beer.
Public domain image retrieved via Wikimedia Commons.

All that beer had one thing in common: it all used strains of yeast that preferred to ferment sugars under warmer conditions. These so-called “top-fermenting” or “ale” yeasts, nearly always strains of Saccharomyces cerevisiae⁴, had a tendency to result in a cloudy beer, and to leave behind often-unwelcome⁵ flavors in the beer through the production of esters if the temperature was too high or not stable enough during fermentation. They also didn’t keep very long, developing a funky, “skunked” taste soon after they were brewed, as undesirable bacteria and yeasts moved in to metabolize sugars and proteins that S. cerevisiae left behind.

The stability problem was helped by the addition of hops, perfected sometime in the 13th century. The mildly antibiotic properties of hops help to keep bacteria and non-brewing yeasts from getting a foothold in the fermenting beer, while allowing the brewing yeast, usually introduced as a well-established colony via a starter, to ferment the wort without competition⁶. Despite this innovation, however, even hopped beers had a tendency to deteriorate over time, and many people didn’t care for the flavor they imparted to the beer.

Something changed in the late 15th century, though. Those Bavarian brewers I spoke of earlier started making a new type of beer. Brewed in caves, deep cellars, or even under blocks of ice, lager, as the new beer was called, could be fermented at much lower temperatures than previous beers⁷. Brewing at lower temperatures meant that other organisms were too sluggish to reproduce in the wort to levels that resulted in off-flavors, and caused grain proteins as well as the yeast itself to fall to the bottom of the beer more quickly and completely, resulting in a clearer, more stable brew, with a cleaner taste. These lagers actually benefited from prolonged storage, as long as the temperature was kept low, becoming clearer and cleaner with time, and they were often stored for six months or more⁸.

Today lagers are the most-often drunk beers in the world.
Public domain image retrieved via Wikimedia Commons.

It wasn’t until the 19th century that the fermenting action of yeast would become understood, and the various strains of it used in brewing classified. In 1883, a chemist working for Carlsberg brewery isolated the strain of yeast responsible for lager. He called it Saccharomyces carlsbergensis, but today it is more commonly known as S. pastorianus.

Ben Franklin supposedly said beer is proof that God loves us. Does that mean yeast are God?
Public domain image retrieved via Wikimedia Commons.

What happened in the 15th century to cause S. pastorianus to be discovered and rise so quickly to prominence in brewing? For many years, it was believed that S. pastorianus was a hybrid of S. cerevisiae and another strain of yeast (or sometimes two or more). Saccharomyces bayanus, Saccharomyces monacensis, and Saccharomyces uvarum, all used in wine and cidermaking, have each been proposed at one time or another as “second parents” to S. pastorianus, but with the advent of genetic sequencing in the 20th century, it became clear that none of those strains were a perfect fit. Each strain left too many stretches in the S. pastorianus genome that couldn’t be traced to either proposed “parent”, leading some researchers to postulate that another, heretofore undiscovered, strain of Saccharomyces must have been responsible, and others to suggest that the new strain must have undergone a large number of mutations before finding its home in some lucky brewer’s fresh wort.

That is, until 2011, when an international group of researchers announced that they had identified a likely candidate for the mysterious “second strain” of S. pastorinanus‘ parentage – in South America. The group isolated and sequenced Saccharomyces eubayanus, a strain of yeast that likes to live on beech trees in Patagonia (and one that’s yet to be found in the wild in Europe despite extensive searching), and found that it possesses genes that represent 99.5% of those found in S. pastorianus and not in S. cerevisiae, making it a compelling candidate for S. pastorianus‘ ancestry.

But how did S. eubayanus find its way into the breweries of Europe from Patagonia? Likely the same way our friend the striped beakfish got to the waters off of Washington: on a boat. One theory is that fruit flies, attracted to barrels of beer or fruit juice on the earliest European vessels to cross the Atlantic to the Americas, brought the yeast with them, stuck to their feet. From there, S. eubayanus somehow found its way into a brewery (maybe through the reuse of barrels or on a person who visited the brewery soon after getting off the ship) and from there into beer, where it got up close and personal with S. cerevisiae, giving rise to a child strain that was perfectly suited for lagering: able to grow and reproduce in much colder temperatures and to thrive on the mix of sugars and nutrients found in beer wort. Now that’s a hell of a journey.

Footnotes
1.A week being about how long it will take to ferment barley and water into something you could call beer, if you were really hard up or had no tastebuds. <<back
2.And quite probably other places, but I’m not familiar enough with the history of beer outside of Europe to say for sure. <<back
3.Some of them by breweries that still exist, and produce beer, even now; the oldest continuously-operating brewery in the world will celebrate its thousandth birthday in 2040. <<back
4.”Saccharomyces” means “sugar mold”. Try not to think about that the next time you’re enjoying an adult beverage. You’re welcome. <<back
5.But not always: the distinctive “banana and clove” flavor of Hefeweizens is a result of esters produced by the yeast used, for example. <<back
6.This was the reason for the creation of the IPA, or India Pale Ale style: it was highly-hopped so as to help it survive long shipping times from England to India during the colonial period. <<back
7.Today beers fermented at higher temperatures are typically called ales, although the term has meant several different things over the centuries. <<back
8.Modern ales usually undergo a period of conditioning as well, but this became possible due to the 19th-century advent of pasteurization and germ-aware sanitation techniques that prevent the beer from becoming infected with undesirable organisms. <<back

Clearly it has been too long since I’ve visited the good folks at Science in My Fiction. I’ve forgotten how to insert pictures and videos. Do make with the clickie though folks. You will not be disappointed. I promise. I’ve found you some interesting reading accompanied by cool pictures. Our editor here recommended a few topics for me as I get myself back in the groove. My first choice of those topics was “animal pigmentation patterns.” Of course it was! I love any excuse to talk a bit about my beloved cephalopods.

David Gallo: Underwater astonishments  (YouTube) I’ve enjoyed several TED talks. This one covers several sea creatures but also one octopus in particular that does a stellar job of making like algae. I was hunting a video of one octopus I saw ages ago that kept changing from one thing to another. If you happen to see it or remember it, please leave a link in the comments.

Octopus Escape (YouTube) This is another example of an octopus blending in quickly with its environment. You will see the blanket like spans between its tentacles also change color. Here is a cool article about a blanket octopus (RealMonstrosities). It even has a neat video with it.

When you start looking into camouflage  and more specifically animal coloration, you find a history of study going back hundreds of years.  The Wikipedia articles linked in the previous sentence do a great job of discussing the how, why, types, and applications of the topics. I encourage you to read them and chase down the links. Yes, I will make you tangent hounds yet.  Seriously though, much of what I want to cover in this post involves the new things I learned, some comparisons I had between animals and humans, and some loose story ideas.

Now about the title of the post,  when you visit the animal coloration article, the first thing you see is the spotted finned and tailed, striped oriental sweetlips fish hanging out while two  striped cleaner wrasse clear off parasites. According to the article, the sweetlips’ spots signal sexual maturity. While  ”the behaviour and pattern of the cleaner fish signal their availability for cleaning service, rather than as prey.” So much of this leaves me wondering exactly how. In human behavior, uniforms often help convey our participation in a specific profession.

The concept of mimicry was one I recalled from middle school science. In Batesian Mimicry, harmless species imitate the harmful ones. In Müllerian Mimicry, the harmful creatures look like each other. Think bees and wasps here. In everyday life you can convert this to think of various law enforcement agencies resembling each other. Not that they are harmful, but the uniforms are meant to convey authority.

I learned that some frogs change their skin color to regulate body heat. There has to be something here to work with. While others have use melanin to tint their bodies to protect from sunburn. Sound familiar?

Here are three new things I learned from How Animal Camouflage Works (HowStuffWorks):

Chameleons might not only change their color to match their environment and as a matter of signaling, but also to broadcast their mood. My clothes, my hair style, my makeup often are affected by my mood. I wonder what it would be like if I could choose to shift my skin color and hair color by my mood. I wonder what it would be like if these parts of me gave away my feelings. Now imagine what that would be like in the political and diplomatic arenas. I read an anthology a while back about alien life. It had more than one story in which a person communicated with another by changing the tones of their skin.

Nudibranches change their color gradually thanks to a change in diet. I love pizza. I had bad acne as a teenager and young adult. Make of that what you will. I can assure you I turned red while deciding to share it.

Some fish change their appearance by released hormones that react to a change in environment. I know some couples start to look alike after being together a while. I really don’t think I’ve taken on the appearance of the cities I’ve lived in. It could be interesting though. I mean think about the folks that get painted up to support their local sports teams. On a scarier note, I am back to thinking about hormones that can change the way you look without your input on the matter.

One more article for the road: Why do some organisms glow? (KSL.com)This one also includes a cool video. This has to be one of my favorite aspects of this topic. I have always been fascinated with deep sea creatures. I am thinking of covering this topic separately in my next post. Interested? Leave me a comment. Also, share with me some stories in which camouflage played a role. Does this post inspire some story ideas for you? What neat things did you learn from making with the clickie?

Thanks for reading; it’s so nice to be back.

*All links are to Wikipedia unless otherwise noted.

Just when you think you’ve got it figured out: vertebrates use red blood with hemoglobin. The hemoglobin carries oxygen in the bloodstream. Even some invertebrates use hemoglobin, although not octopuses: they have a blue copper-based compound in their blood instead.

And then there’s the icefish. These fish have no hemoglobin, or anything else to bind oxygen in their blood. Instead, they have clear plasma. Because they live in very cold areas, and have very low metabolisms, icefish can get away with having oxygen simply dissolve in their blood.

Scientists have known about the icefish and its clear blood since at least 2006, but it wasn’t until recently that a specimen has been kept in captivity. The Tokyo Sea Life Park has a mating pair of ocellated icefish. Not only do these deep-water Antarctic fish have clear blood, they have no scales.

Just another example of evolution disproving what we think we know, and laughing at our generalizations. There are certain physical and chemical laws involved, of course: this strategy may save the fish the metabolic cost of maintaining hemoglobin and red blood cells, but would only work for a lethargic fish in cold, deep waters.