Visions of Type
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If you live in a city, birds are essentially the only wildlife that you meet during your day.
Depending on where you live, you might come several species without even trying. In Vancouver, on a 10 minute walk around my house, I have a good chance to see rock doves (pigeons), crows, mallars, wigeons, hooded mergansers (if I'm lucky), common starlings, house sparrows (sigh), house finches, song sparrows, red-winged black birds, white-crowned sparrows, bushtits, black-capped chickadees, northern flickers, great blue herons, and the mother-of-all-honkers: Canada geese.
Birds and letters are everywhere—art of nature and man.
Letter forms, on the other hand, are the art that is also everywhere. Every typeface is an artistic expression.
Regardless where you live, sadly, you are likely to come across mutants like Comic Sans, Arial and Times New Roman — odious creatures from the shallows. Try to find Gotham, Gill Sans, Frutiger, or Garamond.
Bird songs can be visualized with a spectrogram — a visualization of the frequency components (vertical axis) in the call as a function of time (horizontal axis).
For example, below is a crop of a recording of the American goldfinch, who eats a potato chip in about 0.5 seconds. And when in flight, he has it with dip.
The full recording from the Cornell Lab Macaulay Library is shown below.
Spectrograms give us detailed insight into the fine structure of a vocalization. For example, the black-capped chicadee's “fee-bee” (or cheeseburger) actually has a very short pause (about 50 ms) in the “bee”, making it more of a “be-e”. Below is a recording of this call.
One of my favourite bird sounds is the “sawing machine” of the marsh wren. They often hide in tall reeds around ponds and lakes, making them hard to spot — by eye, but not by ear!
Mnemonics of bird songs help you remember the call and recognize the bird. It's so much easier to think "Quick, three beers!" — the call of the Olive-sided flycatcher — rather than "Chirp, chirp, chirp."
The mnemonic captures the cadence and repetition scheme of the song. For example, if you listen to the white-throated sparrow you can't help but think that this little guy is trying to tell us something.
French Zonotrichia albicollis: Baisse ta jupe, Philomène, Philomène, Philomène. How differently we hear!
—Madelaine Lemieux (via Twitter)
Potato chip!
American Goldfinch (Spinus tristis)
Here here. Come right here, dear.
Baltimore Oriole (Icterus galbula)
Who cooks for you?
Barred Owl (Strix varia)
Here sweetie.
Black-capped Chickadee (Poecile atricapillus)
Trees, trees, murmuring trees.
Black-throated Green Warbler (Setophaga virens)
Drink your tea.
Eastern Towhee (Pipilo erythrophthalmus)
Are you awake? Me too.
Great Horned Owl (Bubo virginianus)
Qu'est-ce qu-il dit?
Great Kiskadee (Pitangus sulphuratus)
Fire fire. Where where? Here here! See it, see it.
Indigo Bunting (Passerina cyanea)
Clear. Wick, wick, wick.
Northern Flicker (Colaptes auratus)
Quick, three beers!
Olive-sided Flycatcher (Contopus cooperi)
Where are you? Here I am.
Red-eyed Vireo (Vireo olivaceus)
Chubby chubby cheeks. Chubby cheeks.
Ruby-crowned Kinglet (Regulus calendula)
See me, pretty, pretty me.
White-crowned Sparrow (Zonotrichia leucophrys)
Dear sweet Canada Canada Canada.
White-throated Sparrow (Zonotrichia albicollis)
If you love birds and typography, these posters are for you. The mnemonic for the bird's song is presented on a background that proportionally presents the bird's plumage colors.
Some posters create natural sets.
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And if you explore the posters, you just might find the bird too.
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Convolutional neural networks
Nature uses only the longest threads to weave her patterns, so that each small piece of her fabric reveals the organization of the entire tapestry. – Richard Feynman
Following up on our Neural network primer column, this month we explore a different kind of network architecture: a convolutional network.
The convolutional network replaces the hidden layer of a fully connected network (FCN) with one or more filters (a kind of neuron that looks at the input within a narrow window).
Even through convolutional networks have far fewer neurons that an FCN, they can perform substantially better for certain kinds of problems, such as sequence motif detection.
Derry, A., Krzywinski, M & Altman, N. (2023) Points of significance: Convolutional neural networks. Nature Methods 20:.
Background reading
Derry, A., Krzywinski, M. & Altman, N. (2023) Points of significance: Neural network primer. Nature Methods 20:165–167.
Lever, J., Krzywinski, M. & Altman, N. (2016) Points of significance: Logistic regression. Nature Methods 13:541–542.
Neural network primer
Nature is often hidden, sometimes overcome, seldom extinguished. —Francis Bacon
In the first of a series of columns about neural networks, we introduce them with an intuitive approach that draws from our discussion about logistic regression.
Simple neural networks are just a chain of linear regressions. And, although neural network models can get very complicated, their essence can be understood in terms of relatively basic principles.
We show how neural network components (neurons) can be arranged in the network and discuss the ideas of hidden layers. Using a simple data set we show how even a 3-neuron neural network can already model relatively complicated data patterns.
Derry, A., Krzywinski, M & Altman, N. (2023) Points of significance: Neural network primer. Nature Methods 20:165–167.
Background reading
Lever, J., Krzywinski, M. & Altman, N. (2016) Points of significance: Logistic regression. Nature Methods 13:541–542.
Cell Genomics cover
Our cover on the 11 January 2023 Cell Genomics issue depicts the process of determining the parent-of-origin using differential methylation of alleles at imprinted regions (iDMRs) is imagined as a circuit.
Designed in collaboration with with Carlos Urzua.
Akbari, V. et al. Parent-of-origin detection and chromosome-scale haplotyping using long-read DNA methylation sequencing and Strand-seq (2023) Cell Genomics 3(1).
Browse my gallery of cover designs.
Science Advances cover
My cover design on the 6 January 2023 Science Advances issue depicts DNA sequencing read translation in high-dimensional space. The image showss 672 bases of sequencing barcodes generated by three different single-cell RNA sequencing platforms were encoded as oriented triangles on the faces of three 7-dimensional cubes.
More details about the design.
Kijima, Y. et al. A universal sequencing read interpreter (2023) Science Advances 9.
Browse my gallery of cover designs.
Regression modeling of time-to-event data with censoring
If you sit on the sofa for your entire life, you’re running a higher risk of getting heart disease and cancer. —Alex Honnold, American rock climber
In a follow-up to our Survival analysis — time-to-event data and censoring article, we look at how regression can be used to account for additional risk factors in survival analysis.
We explore accelerated failure time regression (AFTR) and the Cox Proportional Hazards model (Cox PH).
Dey, T., Lipsitz, S.R., Cooper, Z., Trinh, Q., Krzywinski, M & Altman, N. (2022) Points of significance: Regression modeling of time-to-event data with censoring. Nature Methods 19:1513–1515.