athletes and human biodiversity

you may have seen these howard schatz photos floating around on the internet the past couple of days. (if you did, you spend too much time online! — if you didn’t, you don’t spend enough time online!) i lifted the ones below from imgur.

all different sizes and shapes of people! neat! (^_^)

left to right: gymnastics, high jump (of course!), trampoline, high jump, triple jump, wrestling [click on images for LARGER views]:

howard schatz 01

long distance running, marathon (i love this guy!), decathalon, marathon (him, too!), running (800m):

howard schatz 02

rhythmic gymnastics (oww!!), sport aerobics (huh?), gymnastics, gymnastics, high jump (uh…yeah), gymnastics:

howard schatz 03

bodybuilding, weightlifting, weightlifting(!), rhythmic gymnastics, rhythmic gymnastics:

howard schatz 04

there’s more @imgur. and on howard schatz’s website.

previously: you, too, can become the fastest man on earth!

(note: comments do not require an email. wolffish!)

your ethnicity…

…they can work that out from your mtdna. with a fr*ckin’ 80-90% accuracy rate! awesome. (don’t ask me what a ‘support vector machine’ is, but it sounds awesome, too!)

it’s “coarse ethnicity” — for instance caucasian, asian, african, hispanic — but still:

Inferring ethnicity from mitochondrial DNA sequence

“Background: The assignment of DNA samples to coarse population groups can be a useful but difficult task. One such example is the inference of coarse ethnic groupings for forensic applications. Ethnicity plays an important role in forensic investigation and can be inferred with the help of genetic markers. Being maternally inherited, of high copy number, and robust persistence in degraded samples, mitochondrial DNA may be useful for inferring coarse ethnicity. In this study, we compare the performance of methods for inferring ethnicity from the sequence of the hypervariable region of the mitochondrial genome.

“Results: We present the results of comprehensive experiments conducted on datasets extracted from the mtDNA population database, showing that ethnicity inference based on support vector machines (SVM) achieves an overall accuracy of 80-90%, consistently outperforming nearest neighbor and discriminant analysis methods previously proposed in the literature. We also evaluate methods of handling missing data and characterize the most informative segments of the hypervariable region of the mitochondrial genome.

“Conclusions: Support vector machines can be used to infer coarse ethnicity from a small region of mitochondrial DNA sequence with surprisingly high accuracy. In the presence of missing data, utilizing only the regions common to the training sequences and a test sequence proves to be the best strategy. Given these results, SVM algorithms are likely to also be useful in other DNA sequence classification applications.”

(but don’t forget — race doesn’t exist. it’s juuuust a social construct….)

(note: comments do not require an email. or a cheek swab.)