Noticed a piece at The Root, The Decade in Race: WTF Was That?:
After the tragedy of 9/11, Arab American stereotypes morph from harmless convenient store owner to new American nigger. The Simpsons’ Apuh is suddenly nowhere near as funny
There really needed to be more said here. The convenience store owners were not usually Arab (though some were), generally, they were South Asian, most often Indian American. “Apuh” (it’s spelled Apu, no “h”) is an Indian American, and is depicted as Hindu on The Simpsons. Also, on the order of 50%* of Arab Americans aren’t Muslim, they’re Christian. Like the governor of Indiana, or Ralph Nader. In other words, a disproportionate amount of prejudice directed against “Arabs” is actually directed against Muslims who dress visibly in a way that marks them as Muslim, no matter their ethnicity, and South Asians who are more visibly non-white than most Arabs, especially Sikhs who “dress like Arabs.”
It is possible that the author of the above piece in The Root knows all this, and he was simply pointing to the fact that Indian Americans and South Asians generally are perceived as Arab, despite reality that they aren’t. But this detail should probably have been stated explicitly, since broad swaths of the public are totally unaware of this.
* The usual assertion is that the majority of Arab Americans are Christian, but the data I’ve seen suggests to me that there is a strong likelihood that sometime in the teens of the 21st century a majority of self-identified Arab Americans (as opposed to those with some Arab ancestry) are likely to be Muslim.
Carl Zimmer has a nice write up of the a new paper in Science which characterizes the nature of the cells which are manifest during devil facial tumor disease. The Tasmanian Devil Transcriptome Reveals Schwann Cell Origins of a Clonally Transmissible Cancer:
The Tasmanian devil, a marsupial carnivore, is endangered because of the emergence of a transmissible cancer known as devil facial tumor disease (DFTD). This fatal cancer is clonally derived and is an allograft transmitted between devils by biting. We performed a large-scale genetic analysis of DFTD with microsatellite genotyping, a mitochondrial genome analysis, and deep sequencing of the DFTD transcriptome and microRNAs. These studies confirm that DFTD is a monophyletic clonally transmissible tumor and suggest that the disease is of Schwann cell origin. On the basis of these results, we have generated a diagnostic marker for DFTD and identify a suite of genes relevant to DFTD pathology and transmission. We provide a genomic data set for the Tasmanian devil that is applicable to cancer diagnosis, disease evolution, and conservation biology.
In Carl’s article, he reports:
The cancer, devil’s facial tumor disease, is transmitted when the animals bite one another’s faces during fights. It grows rapidly, choking off the animal’s mouth and spreading to other organs. The disease has wiped out 60 percent of all Tasmanian devils since it was first observed in 1996, and some ecologists predict that it could obliterate the entire wild population within 35 years.
I think that the ecologists need to be careful here, as the public might think that the cancer itself is going to be the immediate proximate cause of extinction. Rather, it seems more likely that the disease will reduce the numbers of the devils, of which there are on the order of 10 to 100 thousand on the island. And small populations, say less than a 1,000, are subject to random fluctuations in population size which could drive them to extinction (imagine a short-term climatic regime which reduces the food supply). It seems that some individuals are already immune to the disease, so over time if nature took its course the population would probably bounce back. Projecting extinction because of disease necessarily and sufficiently is just part of the linear fallacy, which isn’t really good at predicting over the long term in biological contexts. Australia still has rabbits. It’s called evolution.
The Google Decade Ends: If the search king hasn’t ripped up your business yet, just wait. 10 years is a long time in the tech industry. I wonder which company will be the center of retrospectives in 2010? It seems that the time cycle of the rise & fall of “It” firm is speeding up; from IBM to Microsoft to Google. So perhaps it isn’t even around right now.
Yeah, you read that right. Overweight and obesity in urban Africa: A problem of the rich or the poor?:
Descriptive results showed that the prevalence of urban overweight/obesity increased by nearly 35% during the period covered. The increase was higher among the poorest (+50%) than among the richest (+7%). Importantly, there was an increase of 45-50% among the non-educated and primary-educated women, compared to a drop of 10% among women with secondary education or higher. In the multivariate analysis, the odds ratio of the variable time lapse was 1.05 (p<0.01), indicating that the prevalence of overweight/obesity increased by about 5% per year on average in the countries in the study. While the rate of change in urban overweight/obesity did not significantly differ between the poor and the rich, it was substantially higher among the non-educated women than among their educated counterparts.
Here’s a chart showing the urban/rural difference by nation:
The Properties of Adaptive Walks in Evolving Populations of Fungus:
The rarity of beneficial mutations has frustrated efforts to develop a quantitative theory of adaptation. Recent models of adaptive walks, the sequential substitution of beneficial mutations by selection, make two compelling predictions: adaptive walks should be short, and fitness increases should become exponentially smaller as successive mutations fix. We estimated the number and fitness effects of beneficial mutations in each of 118 replicate lineages of Aspergillus nidulans evolving for approximately 800 generations at two population sizes using a novel maximum likelihood framework, the results of which were confirmed experimentally using sexual crosses. We find that adaptive walks do indeed tend to be short, and fitness increases become smaller as successive mutations fix. Moreover, we show that these patterns are associated with a decreasing supply of beneficial mutations as the population adapts. We also provide empirical distributions of fitness effects among mutations fixed at each step. Our results provide a first glimpse into the properties of multiple steps in an adaptive walk in asexual populations and lend empirical support to models of adaptation involving selection towards a single optimum phenotype. In practical terms, our results suggest that the bulk of adaptation is likely to be accomplished within the first few steps.
I’ve discussed this issue before. The general logic here is that when a population is subject to new selection pressures it uses whatever tricks and tools are handy in the short term even if they’re suboptimal in the long term. Over time adaptation should “refine” the phenotype so that there are fewer trade-offs so that fitness gradually converges upon an idealized peak. Consider the various malaria adaptations, which arose in the past 5,000 years, some of which still have major side effects such as sickle cell anemia in homozygotes. But in a malarial environment the side effects, the risk of morbidity and mortality, is worth the overall the reduction in mortality. One imagines that over time new mutations would emerge to mask the deleterious consequences of new adaptations, which are basically evolutionary kludges.
They illustrate this process experimentally:
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The Massive Stock Market Rally of 2009 Ends Today:
In what the Wall Street Journal calls “a comeback of historic proportions,” the U.S. stock market’s banner year closes later on today. The paper says, “With one trading day remaining in 2009, the Dow is on track for its biggest annual gain since 2003, when it rose 25%. It finished Wednesday up 3.1 points, at 10548.51, a fresh peak for the year and the highest since October 2008.” Leading its business section, New York Times also takes note of this year’s rallying stock markets, which “will ring out one of their most volatile periods in history” in a few hours….
Earlier this year a friend of mine argued that we were going through a bear market rally. It seemed a very defensible position to me. But earlier this month I sent him a link to this chart:
The current trend is the dark blue. If this is a bear market rally, this is an unprecedented one. It would be the longest and most robust bear market rally on record. On the other hand, recent macroeconomic events have been somewhat unprecedented. I don’t really see where this rally is based on the soundness of the economic fundamentals of the American economy. Before some might have argued that the efficient wisdom of the market was giving us a signal to which we should pay heed, but the American (and to some extent world) economy has been through two exuberant bubbles in the past 10 years. There’s a flaw in the short term logic, so to speak. The market may point in the right direction in the long run, but in the short run we might still be screwed.
My friend is putting his money where his mouth is, so I tend to listen closely to his judgement as I know he is more than simply talk. I’m sure that readers also have opinions and are making decisions appropriately, so I’m curious the word out on the street is.
If you missed it, you can still watch it online.
Disease Gene Characterization through Large-Scale Co-Expression Analysis:
Celsius, the largest co-normalized microarray dataset of Affymetrix based gene expression, was used to calculate the correlation between all possible gene pairs on all platforms, and generate stored indexes in a web searchable format. The size of Celsius makes UGET a powerful gene characterization tool. Using a small seed list of known cartilage-selective genes, UGET extended the list of known genes by identifying 32 new highly cartilage-selective genes. Of these, 7 of 10 tested were validated by qPCR including the novel cartilage-specific genes SDK2 and FLJ41170. In addition, we retrospectively tested UGET and other gene expression based prioritization tools to identify disease-causing genes within known linkage intervals. We first demonstrated this utility with UGET using genetically heterogeneous disorders such as Joubert syndrome, microcephaly, neuropsychiatric disorders and type 2 limb girdle muscular dystrophy (LGMD2) and then compared UGET to other gene expression based prioritization programs which use small but discrete and well annotated datasets. Finally, we observed a significantly higher gene correlation shared between genes in disease networks associated with similar complex or Mendelian disorders.
Citation: Day A, Dong J, Funari VA, Harry B, Strom SP, et al. 2009 Disease Gene Characterization through Large-Scale Co-Expression Analysis. PLoS ONE 4(12): e8491. doi:10.1371/journal.pone.0008491