switch to utf8 bases

diff --git a/posts/supercomputer_wishlist.mdwn b/posts/supercomputer_wishlist.mdwn
index 7d829effd376ca5bedca7bbe0838e75b2af69d73..9969907a70f4323798ab8ff5c4bb158a0feabcba 100644 (file)
--- a/posts/supercomputer_wishlist.mdwn
+++ b/posts/supercomputer_wishlist.mdwn
@@ -1,8 +1,8 @@
 [[!meta title="Bioinformatic Supercomputer Wishlist"]]
 
 Many bioinformatic problems require large amounts of memory and
 [[!meta title="Bioinformatic Supercomputer Wishlist"]]
 
 Many bioinformatic problems require large amounts of memory and

-processor time to complete. For example, running WGCNA across 10^6 CpG
-sites requires 10^6 choose 2 or 10^13 comparisons, which needs 10 TB
+processor time to complete. For example, running WGCNA across 10⁶ CpG
+sites requires 10⁶ choose 2 or 10¹³ comparisons, which needs 10 TB

 to store the resulting matrix. While embarrassingly parallel, the
 dataset upon which the regressions are calculated is very large, and
 cannot fit into main memory of most existing supercomputers, which are
 to store the resulting matrix. While embarrassingly parallel, the
 dataset upon which the regressions are calculated is very large, and
 cannot fit into main memory of most existing supercomputers, which are


@@ -10,11 +10,11 @@ often tuned for small-data fast-interconnect problems.
 
 Another problem which I am interested in is computing ancestral trees
 from whole human genomes. This involves running maximum likelihood
 
 Another problem which I am interested in is computing ancestral trees
 from whole human genomes. This involves running maximum likelihood

-calculations across 10^9 bases and thousands of samples. The matrix
+calculations across 10⁹ bases and thousands of samples. The matrix

 itself could potentially take 1 TB, and calculating the likelihood
 across that many positions is computationally expensive. Furthermore,
 an exhaustive search of trees for 2000 individuals requires 2000!!
 itself could potentially take 1 TB, and calculating the likelihood
 across that many positions is computationally expensive. Furthermore,
 an exhaustive search of trees for 2000 individuals requires 2000!!

-comparisons, or 10^2868; even searching a small fraction of that
+comparisons, or 10²⁸⁶⁸; even searching a small fraction of that

 subspace requires lots of computational time.
 
 Some things that a future supercomputer could have that would enable
 subspace requires lots of computational time.
 
 Some things that a future supercomputer could have that would enable