NYTimes: What Your DNA Reveals About the Sex Life of Neanderthals
February 26, 2026How Jane Austen revealed the economic basis of society
February 26, 2026https://www.economist.com/christmas-specials/2025/12/12/how-jane-austen-revealed-the-economic-basis-of-society QT:{{” These numbers would all have meant something to Austen’s original readers, argues Mr Copeland, serving as a useful “shorthand” for rank and station. £100 a year was required to afford a single maidservant—“a stout girl of all works”. At £400, a household could employ a cook, housemaid and perhaps a boy servant.
Roughly £700-£1,000 a year was required to keep a carriage. With the help of Highbury’s hypochondriacs, this prize falls within the sights of Dr Perry in “Emma”. The higher income target of £2,000 is eventually met by Marianne in “Sense and Sensibility” when she marries Colonel Brandon. That amount will cover a “proper establishment of servants, a carriage, perhaps two”, and horses for hunting. To satisfy the greater demands of a Mary Crawford, eager for a second home in London, would take at least £4,000 a year. “}}
NYTimes: Super-Agers’ Brains Have a Special Ability, New Study Suggests
February 26, 2026Concepts, estimation and interpretation of SNP-based heritability – Nature Genetics
February 22, 2026See Box 1, viz:
QT:{{”
Box 1 Statistical model used in the GREML approach to estimate hS2NP The statistical model used by GREML can be described in its simplest form as y = Wu + e
where y is an n x 1 vector of standardized phenotypes with n equal to the sample size, W = {wij} is an n x m standardized SNP genotype matrix where m is the number of SNPs, u = {ui} is an m x 1 vector of the additive effects of all variants when fitted jointly in the model, u ~ N(0,Iσ2) with I being an identity matrix, u and e is a vector of residuals, e ~ N(0,Iσ2). An equivalent model is….
y=g+e
g ~ N(0,A…)
A=W W’
In practice, A is called the SNP-derived genetic (or genomic) relationship matrix (GRM) and is estimated from the SNP data. The estimate …from GREML can be described as the estimated variance explained by all the SNPs (mσu) or equivalently as the estimated genetic variance by contrasting the phenotypic similarity
between unrelated individuals to their SNP-derived genetic similarity “}}
https://www.nature.com/articles/ng.3941
Yang, J., Zeng, J., Goddard, M. E., Wray, N. R., & Visscher, P. M. (2017). Concepts, estimation and interpretation of SNP-based heritability. Nature Genetics, 49(9), 1304–1310.
https://doi.org/10.1038/ng.3941
The Condo Market Hasn’t Been This Bad in Over a Decade – WSJ
February 22, 2026https://www.wsj.com/economy/housing/the-condo-market-hasnt-been-this-bad-in-over-a-decade-9f3e7256?st=pN7PSL&reflink=article_gmail_share 10% down this year nationwide
Yale School of Medicine Ranks 4th in U.S. for NIH Funding | Yale School of Medicine
February 22, 2026MCB111 Mathematics in Biology
February 22, 2026http://mcb111.org/w06/w06-lecture.html
Has some nice textbook downloads – e.g.
http://mcb111.org/w06/KollerFriedman.pdf
QT:{{”
There are many good books to learn about probabilistic models. “Probabilistic graphical models: principles and techniques” (by Koller & Friedman) is a comprehensive source about more general probabilistic models than the one we are going to study here.
“}}
Subset of chap 7 focuses on GMRF
Send ppt from our chat
February 22, 2026good tutorial/textbook chaper/review paper on Poisson regression :
Below are a few readings that discuss how to fit a generalized linear mixed model.
1. Breslow & Clayton (1993), JASA, Approximate Inference in
Generalized Linear Mixed Models.
https://doi.org/10.2307/2290687
A classic statistical paper introducing the Laplace approximation and penalized quasi-likelihood for GLMMs
2. Bates (2011), Mixed models in R using the lme4 package Part 5: Generalized linear mixed models
https://lme4.r-forge.r-project.org/slides/2011-03-16-Amsterdam/5GLMMH.pdf 3. Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects models using lme4. Journal of statistical software, 67, 1-48.
https://doi.org/10.18637/jss.v067.i01
4. Bates (2025), Computational methods for mixed models
https://cran.r-project.org/web/packages/lme4/vignettes/Theory.pdf
These are written by the authors of the lme4 package, discussing the details of how a mixed-effects model (more specifically, a generalized linear mixed-effects model) is trained using the PIRLS approach.
Master Equation Notes – Following Up on Your Questions
February 22, 20261. Paulsson (2005) – “Models of stochastic gene expression”
https://www.sciencedirect.com/science/article/abs/pii/S1571064505000138 Nice pedagogical review of the master equation framework – covers the conceptual foundations and different analytical approaches.
2. Shahrezaei & Swain (2008) – “Analytical distributions for stochastic gene expression”
https://pubmed.ncbi.nlm.nih.gov/18988743/
This one derives the exact analytical solutions to the master equation for protein/mRNA distributions.
The Paulsson paper is probably better as a tutorial.
However, it’s a bit difficult to connect to protein & mRNA.
linkstream2 microblog 