Science gives us a "policy forum" this week on gene doping. The lead author, Theodor Friedmann, is the chair of the "Gene Doping Expert Group" at the World Anti-Doping Agency.

The essay describes the current potential of gene doping, and is a good short review. I found the paragraphs on "marketing gene doping" very interesting:

Athletes are an especially vulnerable population in the marketing of performance enhancement (31). Reputable athletes or coaches with little knowledge of genetics are at a disadvantage in assessing "scientific" claims that appear in advertisements. Marketing is particularly worrisome when the science is still a work in progress, when a person's health can be adversely affected, and when consumer knowledge about genetics is low. Although advertisements promoting products that promise to enhance athletic performance have pervaded the Internet for many years, recently it has become home for advertisements that promote products to "alter muscle genes...by activating your genetic machinery" (32), or that state "your genetic limitations are a thing of the past!" (33) or "Finally, every bodybuilder can be genetically gifted!" (34).

These are people who make easy targets for nutrigenomics and other questionable areas of "health enhancement." A large number of amateur bodybuilders and fitness enthusiasts create a "gray market" supporting questionable products, and these products themselves support the ecosystem of effective performance enhancement drugs. They also create a lot of biochemical noise for those who want to find new tests for performance enhancers.

The essay includes a few paragraphs that describe the prospects of future detection of gene doping. It may be very difficult. Detecting some synthetic performance-enhancing agents requires the cooperation of primary producers, who add tracers to their products. Gene doping might enhance performance for periods months or years after the vector is administered, and may not require dosage that would significantly alter isotopic or chemical signatures, even if they contained such tracers. The authors suggest that the incidental biochemical effects of gene doping might enable the identification of a "signature" of such products:

For instance, exposure of murine myoblasts to IGF-1 has been shown to induce transcriptional and proteomic changes that may eventually constitute a "signature" specific for exogenous IGF-1 exposure (29, 30). Of course, the application of these kinds of global assays would require rigorous validation of a connection with specific doping agents or methods.

I am very skeptical -- it's likely that the "signature" of a doped individual will not differ appreciably from the normal variability of tissue metabolism, particularly in the subset of high-performance athletes. Still, it may be possible to find one particular tissue type that provides a high-information-content message about normal versus doped processes. I just think that a lot of innocent athletes are likely to get snared in this net as it closes in on clinical validation.

References:

Friedmann T, Rabin O, Frankel MS. 2010. Gene doping and sport. Science 327:647-648. doi:10.1126/science.1177801

The value of long-term field studies: Christophe Boesch and colleagues report on adoption in the Taï Forest chimpanzee study population -- where more than 30 years of observations have produced 18 well-defined cases of adoption of orphaned individuals. They considered "adoption" to be the provision of maternal care (e.g., carrying, feeding, food sharing, defense) for more than two months. It's possibly unfortunate terminology, as it leads to headlines like mine. Yet it is really interesting behavior.

It would be nice to say that these cases represent 18 happy endings, but these adoptions did not increase the probability of survival compared to orphaned individuals who did not receive ongoing care. There were a couple of cases where females breastfed orphaned infants "for many years," but there seem to be several sad stories too.

Sometimes, the care for the orphaned juveniles was given by males:

Remarkably, all adult males of the East Group that adopted young orphans went a step further by investing in unweaned small infants and carrying them dorsally during travel for many months (see Figures 3 and 4 of Porthos with Gia) (Table 3). Since, Taï chimpanzees walk about 8 km per day on average, this represents a notable investment. Porthos' adoption of Gia lasted for 17 months, until his death due to Anthrax, and he was seen to carry her even in extremely risky situations, such as during encounters with neighboring communities [26]. Furthermore, some males were seen to share their night nest with their adopted infant (Table 3). Fredy, the 3rd ranking male of the East Group, adopted Victor, the son of Vanessa, who died from Anthrax in late December 2008, and shared his nest with him every night, carried him on his back for all long travels, and shared the Coula nuts he opened from December 2008 to July 2009. For example, on February 17th, Fredy cracked 196 Coula nuts for 2h05mn and shared pieces of 79% of them. This gives a measure of the altruistic investment made in an unrelated infant.

That sounds pretty amazing. I think it's very relevant to human evolution, as orphaning must have been very common with the high mortality rates of the past.

The authors propose that adoption is basically a side effect of prosocial behavior in these chimps brought on by leopard predation:

[T]he resulting high predation pressure exerted by these cats seems to have promoted strong within-group solidarity in the form of care for all injured individuals as well as joint coalition defense against the leopards [16], [26]. Once established, this care for the welfare of others seems to have been generalized to new social contexts, including adoption [26]. Any discussions about the evolution of altruism must include the caveat that dissimilar socio-ecological conditions will lead to important population differences in both chimpanzees and humans and we need to remain very careful before making any claims about species differences.

Well, if there was such a simple psychological correlation between various kinds of prosocial behavior, it would make life a lot easier for those of us trying to figure out what happened to humans.

References:

Boesch C, Bolé C, Eckhardt N, Boesch H. 2010. Altruism in forest chimpanzees: the case of adoption. PLoS ONE 5:e8901. doi:10.1371/journal.pone.0008901

Dario Floreano and Laurent Keller describe experiments that combine genetic algorithms and robots. It's a review essay rather than a description of new research, but unlike most descriptions of "evolutionary robotics", it's actually directed toward biologists instead of AI researchers.

In this essay we will examine key experiments that illustrate how, for example, robots whose genes are translated into simple neural networks can evolve the ability to navigate, escape predators, coadapt brains and body morphologies, and cooperate. We present mostly—but not only—experimental results performed in our laboratory, which satisfy the following criteria. First, the experiments were at least partly carried out with real robots, allowing us to present a video showing the behaviours of the evolved robots. Second, the robot's neural networks had a simple architecture with no synaptic plasticity, no ontogenetic development, and no detailed modelling of ion channels and spike transmission. Third, the genomes were directly mapped into the neural network (i.e., no gene-to-gene interaction, time-dependent dynamics, or ontogenetic plasticity). By limiting our analysis to these studies we are able to highlight the strength of the process of Darwinian selection in comparable simple systems exposed to different environmental conditions.

Some of the simplest machine learning experiments are basically like those used in behavioral psychology -- put the robots in a maze, make them remember where the food is, that sort of thing. Robots are simpler than rats, so the researchers can reverse-engineer the "evolved" software at the end of a series of experiments to see what worked and why:

Interestingly, the driving speed of the best-evolved robots was approximately half of the maximum possible speed and did not increase even when the evolutionary experiments were continued for another 100 generations. Additional experiments where the speed was artificially increased revealed that fast-moving robots had high rates of collisions because the 300-ms refresh rate of the sensors did not allow them to detect walls sufficiently in advance at high speed. Thus, the robots evolved to move at intermediate speeds because of their limited neural and sensory abilities.

Figuring out that particular optimization would drive a team of human programmers crazy. Can you imagine? "Why do they keep running into that wall?!"

On the other hand, dumb selection took a lot of generations to get to that point. You can't say selection was more efficient. If you had a crew of programming grunts and forced them to sit in a room for 100 robot generations, they'd come up with something.

It's quite possible that a human would have come up with much better software, by pushing the robots past the limits of mutations on their "genomes". Selection has its own "sensor limitations", it can get stuck in a local optimum, and depends on the mutation structure to explore the landscape.

It helps if the landscape has some strong correlation structure. That's what came to my mind as I read their account of experiments to make robots cooperate:

However, when the arena contained both large and small tokens, the behaviour of robots was influenced by the group kin structure. In groups of unrelated robots (i.e., robots whose genomes where not more similar within than between groups), robots invariably specialised in pushing the small objects, which was the most efficient strategy to maximise their own individual fitness them (i.e., large tokens provided an equal direct payoff as a small token but were more difficult to successfully push). By contrast, the presence of related robots within groups allowed the evolution of altruism. When groups were formed of “clonal” robots all having the same genome, individuals primarily pushed the large tokens even though it was costly, in terms of individual fitness, for the robots pushing (Video S6).

If you wonder how robots have "kin", it's that they share similar (or the same) genomes. The simplicity of the behaviors suggests a functional explanation for kin selection -- for many kinds of tasks, it may simply be easier to cooperate with other individuals who "work" the same way. Different approaches to the same task may clash.

They describe a similar result for cooperation by information sharing:

Similar results were obtained in experiments where groups of light-emitting, foraging robots could communicate the position of a food source at a cost to themselves because of the resulting increased competition near food. In these experiments, robots again readily evolved costly communication when they were genetically related, but altruistic communication never evolved in groups of unrelated robots when selection operated at the individual level [38],[39].

The next logical step for this kind of research is nano-scale: evolutionary robotics on molecular machines. Which is scary. I hope they have the sense not to train them up by eating biological systems...

There's this old course on the books here, "Human aspects of robotics". I suppose it was taught back in the 80's when robots looked like they would replace all the manufacturing workers. I've often thought that someday it may be revived as with robots as the heroes instead of the villains.

References:

Floreano D, Keller L. 2010. Evolution of adaptive behavior in robots by means of Darwinian selection. PLoS Biol 8:e1000292. doi:10.1371/journal.pbio.1000292

Science has a short essay by Terry Harrison this week about Miocene ape evolution: "Apes among the tangled branches of human origins."

This is the sort of article that shows just how frustrating the Miocene apes can be. It's short, probably not even 1500 words, and it has more than 20 genus names stacked into it -- and he doesn't even list Proconsul and its Early Miocene ilk.

I've been trying to give a longer account of Miocene ape evolution here, by dribs and drabs. You can get most of the list under my "Miocene" category. Last fall's account of "Late Miocene apes from Africa" expands significantly on a topic that Harrison glosses -- what do we know about African apes during the time that chimpanzees, gorillas, and humans were diverging?

There are two phylogenetic issues related to Miocene apes that command a lot of attention. Where did hominins come from? -- that's one issue. The other is, how are Asian and African apes related?

This latter question is important because it helps to establish the timeline of orangutan divergence from us and subsequent evolution. It also determines the position of the European apes -- and thereby, whether their locomotor and dietary diversity is relevant to later ape evolution.

Harrison comes down in favor of the hypothesis that Dryopithecus and most other European apes were stem hominids -- that is, collaterals of all living great apes, not specially related to the African apes and humans. The main alternative to that view is that the European apes mostly represent the African side of an early Asia-Africa biogeographic split, such that chimpanzees, gorillas and humans (the hominines) descend from Dryopithecus or some similar lineage closely aligned with these European apes. Harrison is willing to admit the Late Miocene Greek ape Ouranopithecus to the African ape lineage, but in his view the earlier European apes belong to one or more side-branches of the great apes.

A question: If Harrison is correct here, does that affect the paleontological evidence for the human-orangutan divergence? That is, if Dryopithecus were a hominine, it is plausible that the Asian/African ape divergence actually happened in Eurasia not long before 12 million years ago. If Dryopithecus branched off before the African and Asian ape divergence, and Sivapithecus was derived from an African ape, does that make the divergence earlier or later?

I'll consider that over the weekend.

References:

Harrison T. 2010. Apes among the tangled branches of human origins. Science 327:532-534. doi:10.1126/science.1184703

This week's Science is featuring an essay by the first winners of the "SPORE" competition, the team behind the Learn.Genetics and Teach.Genetics websites. Every month Science is going to recognize an excellent online science learning tool, focused on improving science education. I say, good for them!

The essay (free), by Louisa Stark and Kevin Pompei of the University of Utah's Genetic Science Learning Center, describes all the work that goes into their development of "learning modules":

Our development process for a module begins with a summer workshop, advertised to teachers through our e-mail list. An online application enables us to select an outstanding group of 12 to 18 grade-appropriate teachers who represent a diversity of teaching experience, student populations, and locales; about 5 to 10% of applicants are accepted. Participants receive travel expenses and a stipend.

A typical 4-day summer workshop begins with talks by scientists and discussions of scientific articles, from which participants distill important concepts for their students. The teachers and our staff work together to define the "big ideas" that emerge from these concepts, around which the module will be organized. Small groups of teachers then develop each big idea, drafting online and classroom learning experiences designed to assist students in learning. The workshops offer a rare opportunity for teachers to develop creative ideas for curriculum materials that will be used worldwide, to interact with scientists, to update their content knowledge, and to work with other teachers from across the country. A glimpse into one summer workshop can be seen at http://learn.genetics.utah.edu/content/epigenetics/credits/. In it, teachers describe ideas that became the Insights From Identical Twins movie and "Gene control" interactive animation on Learn.Genetics and the "DNA and histone model" activity on Teach.Genetics.

After the summer workshop, our team works with the materials the teachers drafted. Ideas may be combined, modified, expanded or contracted, as we plan a module that addresses the big ideas while fitting the anticipated cost within the available budget.

When I read that, I was a little skeptical that this process would result in anything very useful. Sounds too much like "design by committee". And indeed, when you look at the website, at first glance it's visually bland and hard to read. It's arranged in a tabular format, little color, tiny images, and very small text. This would be a tough website for people with sight problems.

But the information in each of the modules is quite good. And in particular, the little animated slideshows are effective in directing attention to a limited number of points. It strikes me as a better way to get the basics across than if they had put the same information into a short text.

For example, I went first into their "Personalized Medicine" module. It has a little section called "Making SNPs Make Sense", which has a little slideshow animation about SNPs, haplotypes and drug reaction animations. The information is all basically correct and appropriately simplified (they illustrate only three-SNP haplotypes and gloss over the idea of population frequencies, for example. The final section, applying an example of drug response to albuterol, makes sense but is speculative -- it would be hard for a student to know which parts are real science and which parts are projection about what might be true in the future. But I think the whole thing was admirably easy to understand, and I would expect students to come away at least knowing the basics of SNPs. Meanwhile, the additional text after the animation uses consistent graphics to give a few more details about SNP discovery and association by linkage.

That's just one example; there are many other online tools here that could be pulled up on classroom computers. I especially like the "Make a Karyotype" exercise -- it's like "Sesame Street" science, and the website provides printable PDFs for this and many other potential in-class exercises. One of the best is "Cell Size and Scale", which has a gives you a zoomable image starting from ordinary objects and scaling down past different single-celled organisms, human cell types, proteins, and all the way down to atoms and molecules. Other parts of the site don't have animated features but are still interesting -- the short article on PTC tasting polymorphisms is a good example.

There could be more links to sites that give further information. The National Library of Medicine Science Primer would be a next logical step for students who wanted to learn more, and there are many others. I continue to think that the text is too small, and that the site should incorporate more narrative elements. Some students will learn about inheritance more effectively by hearing stories about traits in families, not only by watching little cartoon people pass candy necklaces to their cartoon offspring.

Overall I was quite impressed by Learn.Genetics and Teach.Genetics as resources for teachers. Bruce Alberts' editorial promises another site as part of the "SPORE" series each month.

References:

Alberts B. 2010. Science education web sites. Science 327:504. doi:10.1126/science.1187267

Stark LA, Pompei K. 2010. Making genetics easy to understand. Science 327:538-539. doi:10.1126/science.1183029

New Scientist has an article by Mark Buchanan discussing horizontal transfer as a mechanism for the evolution of early life: "Horizontal and vertical: The evolution of evolution"

There's a lot of "evolution doesn't work the way we thought" stuff in the article, which focuses on Carl Woese:

How could modern biology have gone so badly off track? According to Woese, it is a simple tale of scientific complacency. Evolutionary biology took its modern form in the early 20th century with the establishment of the genetic basis of inheritance: Mendel's genetics combined with Darwin's theory of evolution by natural selection. Biologists refer to this as the "modern synthesis", and it has been the basis for all subsequent developments in molecular biology and genetics. Woese believes that along the way biologists were seduced by their own success into thinking they had found the final truth about all evolution. "Biology built up a facade of mathematics around the juxtaposition of Mendelian genetics with Darwinism," he says. "And as a result it neglected to study the most important problem in science - the nature of the evolutionary process."

In particular, he argues, nothing in the modern synthesis explains the most fundamental steps in early life: how evolution could have produced the genetic code and the basic genetic machinery used by all organisms, especially the enzymes and structures involved in translating genetic information into proteins. Most biologists, following Francis Crick, simply supposed that these were uninformative "accidents of history". That was a big mistake, says Woese, who has made his academic reputation proving the point.

I don't see any inconsistency between the modern synthesis and the idea of horizontal gene transfer. This is a failure of history -- of people reading only Ernst Mayr as a representative of the synthetic view. Other voices -- especially Stebbins -- emphasized gene transfer. The dynamics of genes themselves, as opposed to genes as mere parts of organisms, surely underlie the next generations of evolutionary theoriests, including Dawkins' gene-centric perspective, and Williams' idea of "levels of selection".

Woese is working to discover modes of evolution of gene (and even sub-gene) replicators, before the "hardening" of genomes into organisms. Before the organismal level of selection existed, there can only have been the gene level (taking "gene" to mean replicating element). That's not anti-synthesis, it's what we would expect of replicators at the sub-organismal level.

It's also no surprise as applied to horizontal transfer in more recent lineages. Humans have gotten DNA from viruses during the past few million years, some of which has been fixed in the genomes of the present population. That's no challenge to the way we understand evolution, it's saying that one kind of mutational process is acquisition of viral DNA. Likewise, the introgression of genes between species is no challenge to evolution. It is good evidence that speciation is a evolutionary process -- otherwise boundaries between sister species would be impermeable.

I'm always skeptical when pathologists attempt to diagnose the ills of historical figures. Even if there are medical records or abundant attestations of symptoms from contemporary sources, people in the past had different ways of describing the observations that doctors today collect.

But that doesn't stop people from trying. Last month, John Hayman published a paper in the British Medical Journal that claims a new diagnosis for the lifelong malady that Darwin described in his own journals and correspondence:

Darwin’s symptoms are those of cyclical vomiting syndrome. Although this is primarily a disease of children it may persist into adulthood or may appear for the first time in adulthood. The disease is related to classic migraine and abdominal migraine but is also linked to abnormalities of mitochondrial DNA, with mutations in the MTTL1 gene. This disease is neither well known nor well recognised, particularly in adults, although it was first described in the English literature in 1882.

People with cyclical vomiting syndrome experience abdominal, circulatory, and cerebral symptoms, including headaches and anxiety. Symptoms overlap with those of classic and abdominal migraine, except for a lack of aura. Affected people may experience some or all of these symptoms, with each individual having similar symptoms with each episode. Over time, however, progression or change may occur in the most prominent feature, and episodes may coalesce. Many people report severe motion sickness, and this may be associated with a full episode.

It seems plausible enough, as much so as any retrospective diagnosis could probably be. It bears all the drawbacks of other attempts to diagnose historical figures.

A test of the hypothesis: mtDNA is maternally inherited and haploid, so symptoms are very likely to be shared by maternal relatives:

Darwin’s mother Susannah died with abdominal pain when he was 8. As a child she had vomiting and boils, experienced motion sickness, had excessive sickness during pregnancies, and "was never quite well." Her younger brother Tom had similar symptoms, with headaches, abdominal pains, and motion sickness. A sister, Sarah, considered that Charles and his uncle Tom had the same illness. Evidence of a matrilineal inheritance pattern is good, consistent with an abnormality of mitochondrial DNA.

It's a sad thing to affect a family.

Mitochondrial disorders are increasingly recognized as causes of chronic disease -- just the other day, a new study implicated defective mitochondria as causes of Parkinson's Disease. I think it's hilarious because there is a cadre of geneticists who depend on the notion that mtDNA is a neutral marker of population history.

Darwin's DNA has nothing to say about whether it was neutral on an evolutionary timescale, but every famous mtDNA functional mutation reminds us that there is biological function there, which is a target of selection under some circumstances.

(via Why Evolution Is True)

References:

Hayman JA. 2009. Darwin's illness revisited. Br Med J 339:b4968. doi:10.1136/bmj.b4968

For the past twenty years, there's been a rapid secular trend toward lower birth weight in the U.S.

Between 1990 and 2005, the birth weight of full-term babies in the U.S. declined nearly two ounces to an average of 7 pounds, 7.54 ounces, a reversal of a trend that had seen birth weights climb steadily since the 1950s, according to the study. Babies were also born 2.5 days earlier on average in 2005 than in 1990, the study said.

Two of our kids would be in this sample -- the twins are excluded as multiple births. So I'm interested.

The lower-birth-weight trend couldn't be explained by common factors such as how much weight mothers gained during pregnancy, whether the delivery was induced or by cesarean section, the amount of prenatal care, or maternal-health issues such as smoking and hypertension, researchers said. Only babies born at between 37 weeks and 41 weeks of gestation, which doctors consider full term, were studied.

Researchers repeated their analysis in a sample of low-risk women—healthy, educated Caucasians in their mid-to-late 20s—and found that the decrease in birth weight was even more pronounced, suggesting that the trend wasn't the result of changes in the population of mothers.

My candidate is pregnancy tests. Women now know with much greater accuracy their gestational age, which affects the composition of the sample here. Most of the decline in birth weight happened between 2000 and 2005 (between Sophie and Goodwin!) in the U.S. sample. That coincides with a real rise in early ultrasound and super-early pregnancy detection home kits.

If I'm right, the decline is just an artifact of reducing the noise between 40 and 42 weeks. Fetal weight gain in the last few weeks is around a quarter pound a week. I don't think reducing uncertainty about high-gestational-age births would account for the whole decline, but it could explain a large fraction of it.

Michael Balter reports on a new radiocarbon calibration called INTCAL09. The calibration curve purports to provide a calendar age calibration up to 50,000 years ago for AMS radiocarbon dates.

Balter's report gives a good account of the basics. The atmospheric concentration of carbon-14 varied over time, so that organisms from some ancient times started with a higher proportion and other times started with a lower proportion. The radiocarbon dating technique depends on knowing this initial carbon-14 proportion. But we can only figure this out by comparing the present carbon-14 proportion in things whose ages we know -- like tree rings. Before 25,000 years ago, good non-radiocarbon chronologies are hard to come by, so up to now there has been no good calibration curve.

More recently, however, thanks to new and more accurate data from foraminifers, corals, and other sources--plus some fancy statistical treatments that help predict which way data gaps bend the curve--the INTCAL group has been able to resolve most of the discrepancies. "It took the group quite a while to come together and agree," says INTCAL team leader Paula Reimer, a geochronologist at Queen's University Belfast in Northern Ireland. But the new data, combined with what Reimer calls a "real sense of necessity" among team members to resolve the debates, won the day.

I'm skeptical when I see calibrated dates because they seldom report the calibration error. I like "fancy statistical treatments" that actually report their error. The entire reason a calibration model like INTCAL09 looks good is that it represents only one component of variability within a large set of separate chronometric datasets. The "debates" are more or less about whether that component is time, and if not what other factors must be controlled. Resolving the debates doesn't mean that the model will reduce the error associated with calibrating a given date -- it (hopefully) means that calibrated dates will be unbiased.

In principle, calibration is good because it facilitates comparison between radiocarbon and other dating methods, like OSL or ESR. It also gives a more accurate view of the temporal scale of events -- the radiocarbon chronology compresses the period between 40,000 and 10,000 years ago into 25,000 radiocarbon years instead of 30,000 calendar years. It makes a difference, if for no other reason, because it makes the initial Upper Paleolithic look more rapid than it really was.

Julien Riel-Salvatore ruminates on similar issues ("Paleolithic radiocarbon legerdemain")

The really bad dating problem happens at points where the atmospheric carbon-14 declined. Some declines occurred with nearly the same rate as actual decay of the carbon-14. A younger sample may then up with the same carbon-14 proportion as an older sample, with no way to tell between them. (I discussed this problem as applied to initial Upper Paleolithic-era dates in "Radiocarbon fudgery".)

Because different datasets vary in their results, apparent declines in atmospheric carbon-14 seem more common in those individual datasets than in the model that reflects their common features. The atmospheric carbon should be better reflected by the model -- after all, there's only one atmosphere, so these datasets should reflect the same value.

But any single series of dates ought to have temporal stochasticity more like an individual dataset. When we take dates from bone collagen -- which is not one of the kinds of data with chronological controls -- there ought to be a separate, source-specific error that we can't control by a calibration model.

Does it matter? I think we should assume the resolution of a 40,000-year-old calibrated radiocarbon date is no better than 3000 years. And in some cases more -- depending on the atmospheric trend. If one date is 3000 years earlier than the other, I think there's a very good likelihood that the earlier date really did happen first.

Too conservative? I'd like to see somebody run the numbers on it.