7-7 bombers,who were they?

Seething Unease Shaped British Bombers’ Newfound Zeal.

Related: Profile of Salafi jihadists….

Update: A British Jihadist.

Update II: This part of the article from The Prospect (an interview with a gleeful radical) is interesting:

Taseer: Given that the Koran is incontestable to the letter, and that it is unique because there is no another religion in which there is a text so pure, handed down from God to man, can there be a moderate Muslim?

Butt: No. You’ve hit the nail on the head. If someone believes that it’s the incontestable word of Allah, how can he take a moderate view? We must fight if it is the will of Allah. I don’t want to say that Muslims don’t believe in Allah, but what I will say is that their faith in Allah is weak. They fear man the same way that the Jews feared the pharaoh, who they feared more than Allah and that’s why they were afraid to do anything against him, until Moses came and liberated them. The lack of leadership in the Muslim community is simply because they are too afraid to stand up against this so-called undefeatable giant of the United States.

Taseer: Coming back to the youth, are they angry?

Butt: Many are from quite wealthy families, as I am.

Taseer: So you don’t see this rise of extremism among British Muslims as rooted in economic disadvantage?

Butt: I think that’s a myth, pushed forward by so-called moderate Muslims. If you look at the 19 hijackers on 9/11, which one of them didn’t have a degree? Muhammad Atta was an engineer [he was actually an architect and town planner] at the highest level. His Hamburg lecturer said, “I didn’t have a student like him.” These people are not deprived or uneducated; they are the peak of society. They’ve seen everything there is to see and they are rejecting it outright because there is nothing for them. Most of the people I sit with are in fact university students, they come from wealthy families….

The above is why I have mooted the idea of a new historically contextualized view of the Koran as being an option for some “Muslims.” Right now this is a discounted view, and so you have to push the argument over to one of interpretation on the next level. If you read the interview above though, note how sly some of the redefinitions of terms of the radical are.

Note: Value added comments appreciated!

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Continuity, or not….

Dienekes reports on recent extractions of mtDNA from remains in the Iberian peninsula1…not surprisingly (to me at least) there is continuity between the ancient populations and modern ones. This is relevant to debates about replacement of various aspects of the identity of one people by another. In places like Hungary, Spain or France it seems like there was elite replacement of the substrate language and culture (though not alleles). In contrast, in places like Bulgaria or Assam the substrate absorbed the elite. But, a problem crops up when people try and extend these particular cases to the whole world as if they hold true like a scientific law. For example, the recent story that “Britains have changed little since Ice Age” is a bit too neat, and fits into an archealogical bias that is part of the backlash against excessive typological thinking about “nations” before 1950. That is, the English nation were a volk of one tongue and one blood, which replaced the British ethnos in the 6th century, as hinted in Gildas’ writings (“The barbarians drive us to the sea”). Rather, the archealogical and historical paradigms now tend to presume that the replacement of the British by the English was one of elite cultural imposition. If you read the old post Celts and Anglo-Saxons you will find that the “truth” as suggested by the genetic data that is emerging in the last 5 years is more complex than either replacement or acculturation.2 Strictly speaking the assertion that the peoples of the United Kingdom are descended from Ice Age Northern Europeans is probably correct, because even if there was an influx of alleles from Germany in the 5th and 6th centuries the two populations were not particularly distinct (well, at least in comparison to “Neolithic farmers” from the “Near East”).

Since islands are relatively simple systems (migration is often constrained to choke points) I leave you with a post Japanese origins.

1 – I assume the reference to “Iberians” implies the peoples of the southern half of the peninsula, who in pre-Roman times spoke their own languages unrelated to Celtiberian and possibly distant from the Basque dialects. The people of Tartessos are the most prominent representatives of this cultural complex.

2 – I have read recently that the transition from a Celtic to an Anglo-Saxon peasantry was marked by a shift in the layouts of field and village in much of the east of England. Such changes could be triggered by cultural diffusion, but since the change wasn’t functionally that important it suggests to me that there was some replacement of a Celtic peasantry by Germanic settlers who brought their own traditions and customs.

Not genes and not environment

On many measures, identical (monozygotic, MZ) twins are not in fact “identical”, despite the fact that they share essentially identical DNA and highly similar family environments. Indeed, for some traits, such as personality, all non-genetic effects appear to be of the kind that makes siblings different than one another. Peer socialization is one plausible source of this non-shared environment, but stochastic biological events probably play a role as well.

These stochastic effects are seen prominently in studies of aging in the worm C. elegans. Even when genes and environment are held constant[1], there is considerable variation in lifespan (time of death) within a population. (Almost as much variation in relative lifespan as the human population of the US.) A paper published this week in Nature Genetics (ironic) reports that chance variation in the level of induction of a stress-induced reporter predicts (to some extent) variation in lifespan.

When both genotype and environment are held constant, ‘chance’ variation in the lifespan of individuals in a population is still quite large. Using isogenic populations of the nematode Caenorhabditis elegans, we show that, on the first day of adult life, chance variation in the level of induction of a green fluorescent protein (GFP) reporter coupled to a promoter from the gene hsp-16.2 predicts as much as a fourfold variation in subsequent survival. The same reporter is also a predictor of ability to withstand a subsequent lethal thermal stress. The level of induction of GFP is not heritable, and GFP expression levels in other reporter constructs are not associated with differences in longevity. HSP-16.2 itself is probably not responsible for the observed differences in survival but instead probably reflects a hidden, heterogeneous, but now quantifiable, physiological state that dictates the ability of an organism to deal with the rigors of living.

The astonishing implication is that similar reporters could be found that predict the chance variation in human lifespan (or other dimensions of human biodiversity).

How did they do this research?

First, they created a strain of worm which fluoresces in response to exposure to high temperatures (“heat shock”) (panel “a” below). The “heat-shock gene” hsp-16.2 is expressed when worms are heat shocked. The promoter of hsp-16.2 was joined with the protein coding sequence of green fluorescent protein (GFP) and this construct was integrated into the worm’s genome. The intensity of GFP expression (measured by fluorescent intensity) is variable within an isogenic population (panel e). For their experiments, young adult worms were exposed to a heat shock (panel b) and then some time later they the worms were sorted into high, medium and low GFP subpopulations (panels f-h). The level of GFP expression becomes more variable at later times (panels c-d). The GFP expression level is not heritable: the progeny of worms from the high and low groups are indistinguishable when tested for GFP induction.

After sorting into three subpopulations, worms were tested for lifespan (panels a-c) or thermotolerance (i.e., lifespan at high temperature; panels d-f).

Numerous controls follow. The biggest problem for their study is that heat shock is known to cause increased lifespan on its own. They don’t claim to have overcome this confounding effect, and so it seems to me that differential GFP expression may actually be a marker for this effect.

1 – C. elegans has two sexes: male and hermaphrodite. Hermaphrodites are self-fertilizing. Selfing allows for the production of genetically homogenous populations (except random mutations). Worms are grown on solid media in Petri dishes (“plates”) or in liquid cultures. Worms living on the same plate are essentially experiencing the same environment, but undetected variation may exist between different plates or within different regions of the same plate.

Battle of the disciplines

I’m conflicted, Discovering functional relationships: biochemistry versus genetics:

Biochemists and geneticists, represented by Doug and Bill in classic essays, have long debated the merits of their methods. We revisited this issue using genomic data from the budding yeast, Saccharomyces cerevisiae, and found that genetic interactions outperformed protein interactions in predicting functional relationships between genes. However, when combined, these interaction types yielded superior performance, convincing Doug and Bill to call a truce.

I’ve cut & pasted the text below, it works out in the end.

Introduction

For more than ten years, Doug, a retired biochemist, and Bill, a retired geneticist, have lived on a hill overlooking a car factory, debating their strategies for reverse engineering a car (see: http://www2.biology.ualberta.ca/locke.hp/dougandbill.htm). Doug advocated rolling up his sleeves, getting under the hood and determining how the parts fit together. Bill preferred tying the hands of a different car-factory worker each morning, then relaxing with a cup of coffee and later examining the cars that emerged from the factory.

One day, Doug and Bill strolled over the next hill. In the midst of debate, they encountered Sharyl, a graduate student in computational genomics. Having overheard their debate, she interjected, ‘I don’t know much about cars, but I detect an analogy to biochemistry and genetics. I’m trying to discover functional relationships between genes and proteins in yeast and I wonder which of your strategies would work best.’
Differing approaches to determining gene function

To discover functional relationships, Doug would ask, ‘Which proteins physically interact with my favorite protein?’ By contrast, Bill would perturb the DNA sequence of a gene and observe the consequences in vivo, asking ‘What are the genetic interaction partners of my favorite gene?’ In other words, ‘Which genes produce surprising phenotypes if mutated in combination with my favorite gene?’ Sharyl described how the fields of biochemistry and genetics had ‘gone genomic,’ scaling up their classical approaches to discover functional relationships with ever-greater efficiency. Their resulting systematic studies offered a playing field on which to assess Doug and Bill’s dilemma. Sharyl then wondered, ‘Which type of interaction – protein or genetic – is better at revealing functional relationships?’ She pulled out her laptop computer and set to work (Figure 1).

Protein versus genetic interactions in predicting functional relationships

Because ‘gene function’ is vaguely defined, Sharyl used the Gene Ontology (GO) vocabulary, which describes gene products in terms of biological process, cellular component and molecular function (http://www.geneontology.org/) 1 and 2. She defined three measures of functional relatedness for a pair of genes: (i) shared GO biological process (shared process); (ii) shared GO cellular component (shared component); and (iii) shared GO molecular function (shared function). For example, if two genes were assigned to the same GO biological process category, Sharyl considered the gene pair to have a ‘shared process’. To avoid associations between genes in broadly defined categories, she considered only specific GO categories – those to which 200 or fewer genes (out of not, vert, similar6000 total yeast genes) were assigned, including genes assigned to more specific daughter categories. To represent the biochemists, she chose a high-confidence protein-interaction data set based on affinity purification followed by mass spectrometry (APMS) [3]. For the geneticists, she fielded a recent systematic genetic-interaction data set [4] (Tables 1 and 2 in the supplementary data online; Box 1).

Protein and genetic-interaction screens

Synthetic genetic array (SGA) analysis is a high-throughput method that assesses pairs of genes for genetic interaction 4 and 19. A strain carrying a mutated query gene is crossed to an array of not, vert, similar4700 strains, each mutated in a different non-essential yeast gene. The resulting double mutants are then assessed for fitness. Slow growth or lethality relative to each of the single-mutant strains is declared synthetic sickness or lethality. In the SGA data set used here, 159 query genes were crossed to the array, resulting in not, vert, similar730 000 gene pairs tested for genetic interaction. Based on this data set, the genetic network is between two and 54 times more dense than the protein-interaction network.

Affinity purification followed by mass spectrometry (APMS) is used for high-throughput discovery of physical protein interactions. A ‘bait’ protein is precipitated in a complex with its interacting proteins. Members of this ‘pulled-down’ complex are then identified by mass spectrometry. The two large APMS studies in yeast are known as the tandem affinity purification (TAP) [3] and high-throughput mass spectrometric protein complex identification (HMS-PCI) [6] studies. In both studies, the data can be interpreted in two ways. The spoke interpretation defines an interaction between a bait protein and each protein it pulls down. The matrix interpretation, however, counts interactions between all pairs of proteins pulled down by a bait. In the TAP study, bait constructs were integrated into the yeast genome and expression was controlled by an endogenous promoter. In the HMS-PCI study, however, the bait construct was plamid-borne and expression was controlled by a robust exogenous promoter. Thus, the TAP data set is more likely to be physiologically relevant, although the HMS-PCI study could detect interactions between gene products not normally expressed in the condition examined. The TAP and HMS-PCI data sets employed 1167 and 725 baits, respectively. A gene pair was considered assessed for protein interaction, if at least one gene of the pair was a bait and the other was not filtered out as a ‘promiscuous prey’ [6].

Yeast-two-hybrid (Y2H) is a high-throughput method for assessing direct physical interaction between two proteins (although indirect ‘bridged’ interactions can also be detected). Here our Y2H data set consisted of the union of the interactions reported by Uetz et al. [18] and the ‘core’ version (corresponding to interactions detected at least three times) of the data set produced by Ito et al. [17].

To level the playing field, she considered only the 104 409 gene pairs (the ‘arena’) assessed by both approaches and for which both genes in each pair had a GO annotation. In this arena, the number of gene pairs sharing a specific GO process, component or function was 3841, 1803 and 1139, respectively. The arena contained 48 biochemical interactions and 729 genetic interactions, derived primarily from screens involving the 17 genes used both as baits in the protein-interaction screens and as query genes crossed to 4500 mutants in synthetic genetic array (SGA) analysis. Interestingly, there was no overlap between the protein and genetic interactions (Table 3, supplementary data online). A previous related study [5] did not consider whether gene pairs had been assessed for both types of interaction and used literature-derived interaction data, which are subject to inspection bias.

With a few taps on her k
eyboard, Sharyl let the games begin. Two proteins exhibiting a protein interaction had a shared process, component or function 42% (P=2e-17), 31% (P=2e-15) and 29% (P=1e-16) of the time, respectively. Genetic interactions were uniformly less-accurate indicators of shared function, with corresponding rates of 19% (P=2e-63), 15% (P=2e-66) and 8% (P=2e-28). However, genetic interactions detected gene pairs with shared function with much higher sensitivity (4–6%) than biochemical interactions (0.5–1.2%; Table 4 in the supplementary data online). When considering different physical-interaction data sets 3 and 6 (Box 1), genetic interactions were consistently more sensitive and sometimes more accurate (see Glossary; Table 4, supplementary data online). Thus, it was difficult to declare a clear winner.
Combining genetic and protein interactions with other data

Are genetic interactions combined with other types of evidence more informative than protein interactions combined with other evidence? Rather than considering each type of interaction in isolation, several groups have previously combined heterogeneous data, using machine learning approaches to predict some property of a gene pair or to predict gene function 7, 8, 9, 10, 11 and 12. Therefore, Sharyl combined multiple types of evidence [11] – including co-localization [13], sequence homology [14], correlated mRNA expression 15 and 16 and chromosomal distance (Table 5, supplementary data online) – to predict shared function. She chose a previously described probabilistic-decision tree approach [12] and compared performance with and without the benefit of protein and/or genetic-interaction data. For each of shared process, component, and function and for each choice of input data, she performed cross-validation: she randomized all gene pairs in the arena into four groups, and successively scored each group using a model trained on the remaining three. She then compared the prediction score of each gene pair with its corresponding shared process, function or component status. A plot of true- versus false-positive rates revealed that genetic and protein interactions were comparable at low sensitivities; however, as sensitivity increased, genetic-interaction data enhanced performance more than protein-interaction data. This trend was observed for shared process (Figure 2), component (Figure 1a, supplementary data online) and function (Figure 1b, supplementary data online). Doug, the biochemist, began to despair.

Before Bill could begin to gloat, however, Sharyl showed that genetic- and protein-interaction data together gave markedly better results than either alone, suggesting that each offers distinctly different types of information. Although protein interactions can represent associations between genes in the same complex or physically connected pathway, genetic interactions can additionally reflect relationships between genes in physically non-interacting pathways. She repeated this analysis with another APMS protein-interaction data set [6] and then with the union of two yeast-two-hybrid (Y2H) data sets 17 and 18 (Tables 1 and 3, and Figures 2 and 3 in the supplementary data online), altering the arena appropriately. In each case, genetics beat biochemistry by a slim margin, but the combination of these complementary interaction types outperformed either alone. Sharyl’s results convinced Doug and Bill to shake hands and head back over the hill … until new data or new technology call for a rematch.

Eating their own

Seems like there is some intellectual cannibalism going on over on the Left. The Savage Minds anthropology weblog is being stomped on from all angles of the Progressosphere because they dared to point a sharp object at Jared Diamond. Kerim has the round up. He pointed to us back when Greg & Henry’s paper broke and I appreciate that, but my experience on that weblog is that it has a pretty standard liberal slant, so I’m sure this episode of being broadsided by big names in the Progressosphere must be somewhat surreal. Anyway, the original post that started this actually referenced Guns, Germs and Gonads, you know, to show the racist perspective. Also check out Henry Farrell’s post. I think I agree with him that Ozma does throw around the term “racist” in a cavalier fashion. Shit gets complicated when you are trying to stay on the politically correct side all the time.

Addendum: Since I’ve been a bit priggish about concepts, categories and precision recently, I will admit I probably elided over great differences within what I termed the “Progressosphere” because I’m not too political and my own inclinations are pretty orthogonal to the center to Left axis. So yeah, they aren’t all Muguloos.

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Cajun genetics

Here is a profile of a researcher who has been on the Cajun beat for a while now. If you are a member of a small relatively homogenous group which has weird diseases and keeps decent records, well, expect more of this sort of thing.

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Not a "paradox" at all

The article, The Christian Paradox is making the rounds. It starts:

Only 40 percent of Americans can name more than four of the Ten Commandments, and a scant half can cite any of the four authors of the Gospels. Twelve percent believe Joan of Arc was Noah’s wife…Three quarters of Americans believe the Bible teaches that “God helps those who help themselves.” That is, three out of four Americans believe that this uber-American idea, a notion at the core of our current individualist politics and culture, which was in fact uttered by Ben Franklin, actually appears in Holy Scripture.

Perhaps the readers of Harper’s believe that Christianity is something you find in the Gospels (sola scriptura writ large), but if you are an unbelieving anthropologist you would say that a religion is lived by the people who profess it, and it is out here, not in there. This is relevant to the thread below where we discuss the future of (North) American Islam. If you are a believer in religion X, you are going to assent to creedal assertions a, b, c…, and the intersection of those assertions help define what a religion is fundamentally. But, if you are not a believer a religion is nothing fundamentally except for what the people who espouse it say it is, and to make that judgement you need to weight various semantical nuances in their proper context, ascertaining the character of a religion is not an act of faith but a cognitive process of category creation.

Apropos of this, in the first chapter of Knowledge, Concepts, and Categories I learned that:

  1. People tend to create concepts or categories with OR conditions more than AND conditions (that is, a loose set of probable characteristics rather than a tightly integrated set of necessary traits).
  2. People are aware of the correlated variables within the concept.
  3. Context matters in how people perceive a category.
  4. Repeated input can result in adaption via inductive reasoning so that the center of gravity of a concept can shift over time.
  5. Not all traits have additive effects (ie; not “linearly separable”).
  6. And people tend to attribute essences to a category.

I think the last is a problem in light of public policy disputes because we no longer live in bands of 100 people, we exist in a world where macroscale constructs which exhibit flux and continuity are the norm. 10,000 years ago there might have been 50 Muguloo tribesmen, and you could make pretty robust generalizations of those Muguloos, to the point where a distribution-population way of thinking was unnecessary. Today, you have 12 million Jews, or 1.2 billion Muslims, tens of millions of liberals and conservatives…but we still talk as if they were just a band of Muguloos.1 Additionally, the disjunctive tendency of categories (trait A OR trait B OR trait C) also causes confusions because people disagree about the particulars but never make their axioms explicit so that it is often the norm to just talk past each other. More later….

1 – The closer a category or concept comes to one’s own self-reference the more nuanced, precise and qualified one will get about defining it. Muguloos be damned!

Play with meiosis

One of the main reasons this site is around is to make basic genetic knowledge a casual background feature of the data bases of people who would otherwise not know much about this important science (another reason, at least for me, is to dump a lot of historical and non-science data out there to a scientifically literate audience so they can better form models which influence their view of public policy). So in that spirit, check out Using Karyotypes to Predict Genetic Disorders (it has some neat interactive movies, though if you know genetics, don’t worry about it, but if you have been skipping the science posts for lack of a basic background, I advise you check out the link). Remember, to the first approximation it all starts out on basic Mendelian principles.

The wild "horse" and other knots

Przewalski’s Horse:

Some authorities believe the Przewalski is a direct ancestor of the modern day domesticated horse. Others contend this is not possible as the Przewalski is a different species having sixty-six chromosomes while the domestic horse carries sixty-four. It is possible to cross the Przewalski with the domestic horse, and the resulting hybrid is fertile; however this offspring has sixty-five chromosomes. When crossed again to the domestic horse, the new generation returns to sixty-four chromosomes and little influence of the Przewalski horse is evident.

Related to this, FISH analysis comparing genome organization in the domestic horse (Equus caballus) to that of the Mongolian wild horse (E. przewalskii):

…Previous studies of GTG-banded karyotypes suggested that the chromosomes of both equids were homologous and the difference in chromosome number was due to a Robertsonian event involving two pairs of acrocentric chromosomes in EPR and one pair of metacentric chromosomes in ECA (ECA5). To determine which EPR chromosomes were homologous to ECA5 and to confirm the predicted chromosome homologies based on GTG banding, we constructed a comparative gene map between ECA and EPR by FISH mapping 46 domestic horse-derived BAC clones containing genes previously mapped to ECA chromosomes. The results indicated that all ECA and EPR chromosomes were homologous as predicted by GTG banding, but provide new information in that the EPR acrocentric chromosomes EPR23 and EPR24 were shown to be homologues of the ECA metacentric chromosome ECA5.

Also, Invasive honeysuckle opens door for new hybrid insect species:

The animal family tree may not be filled just with forks, but may also contain knots: hybrid species with two different ancestors rather than one, according to a team of Penn State researchers.

“Hybrid” speciation is pretty common in plants from what I know, but the issues surrounding animals are sketchier….

Related: Breakin’ free of biology?

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