Posts Tagged ‘from’

How evolution builds genes from scratch

March 23, 2025

https://www.nature.com/articles/d41586-019-03061-x

Levy, A. (2019). How evolution builds genes from scratch. Nature, 574(7778), 314–316. https://doi.org/10.1038/d41586-019-03061-x

How evolution builds genes from scratch

Scientists long assumed that new genes appear when evolution tinkers with old ones. It turns out that natural selection is much more creative.

2nd “MBB” dept at Yale

March 19, 2025

Didn’t realize we are the 2nd “MBB” dept at Yale.
(See “admin” section in the below:
https://en.wikipedia.org/wiki/Joseph_S._Fruton
)
+
https://mbb.yale.edu/sites/default/files/files/A%20Scandalously%20Short%20History%20of%20MBB%20at%20Yale%20University(2).pdf

The Abstract: 8 Compounds That Target Aging

March 18, 2025

Guarente, L., Sinclair, D. A., & Kroemer, G. (2024). Human trials exploring anti-aging medicines. Cell Metabolism, 36(2), 354–376. https://doi.org/10.1016/j.cmet.2023.12.007

https://www.cell.com/cell-metabolism/fulltext/S1550-4131(23)00458-8

QT:{{”
In a recent issue of Cell Metabolism, Guarente co-authored a review article about human trials exploring compounds that target pathways and mechanisms of aging along with David Sinclair, Ph.D., one of Dr. Guarente’s postdoctoral mentees and now a professor of genetics at Harvard Medical School, and Guido Kroemer, M.D., Ph.D., a professor at the Université Paris Cité. Guarente and his colleagues focus on eight drugs and compounds: metformin, NAD+ precursors, glucagon-like peptide-1 receptor agonists, TORC1 inhibitors, spermidine, senolytics, probiotics, and anti-inflammatories.
These interventions made the list for four reasons: 1) they’re well-represented in ongoing or completed human clinical trials; 2) they’ve been shown to slow aging in preclinical studies; 3) they’re thought to be sufficiently safe for long-term use in humans; and 4) they work by targeting the hallmarks of aging.
“}}

Growing Up Murdoch – The Atlantic

March 13, 2025

https://www.theatlantic.com/magazine/archive/2025/04/rupert-murdoch-family-succession-james-murdoch/681675/

1906.02691 An Introduction to Variational Autoencoders

March 2, 2025

Has detailed setup for ELBO

https://arxiv.org/abs/1906.02691

Kingma, D. P., & Welling, M. (2019). An introduction to variational autoencoders. Foundations and Trends® in Machine Learning, 12(4), 307–392. https://doi.org/10.1561/2200000056

Unsupervised Feature Learning and Deep Learning Tutorial

March 2, 2025

http://deeplearning.stanford.edu/tutorial/supervised/OptimizationStochasticGradientDescent/

ML | Stochastic Gradient Descent (SGD) – GeeksforGeeks

March 2, 2025

https://www.geeksforgeeks.org/ml-stochastic-gradient-descent-sgd/

Investigating spatial dynamics in spatial omics data with StarTrail | bioRxiv

March 2, 2025

https://www.biorxiv.org/content/10.1101/2024.05.08.593025v1

Chen, J., Xiong, C., Sun, Q., Wang, G. W., Gupta, G. P., Halder, A., Li, Y., & Li, D. (2024). Investigating spatial dynamics in spatial omics data with StarTrail. bioRxiv (Cold Spring Harbor Laboratory). https://doi.org/10.1101/2024.05.08.593025

1803.00567 Computational Optimal Transport

March 1, 2025

https://arxiv.org/abs/1803.00567

explains the dual problem well

Peyré, G., & Cuturi, M. (2018, March 1). Computational Optimal transport. arXiv.org. https://arxiv.org/abs/1803.00567

tutorial_on_optimal_transport.pdf

2312.07511 A Hitchhiker’s Guide to Geometric GNNs for 3D Atomic Systems

March 1, 2025

https://arxiv.org/abs/2312.07511

Duval, A., Mathis, S., V., Joshi, C. K., Schmidt, V., Miret, S., Malliaros, F. D., Cohen, T., Liò, P., Bengio, Y., & Bronstein, M. (2023, December 12). A Hitchhiker’s guide to Geometric GNNs for 3D atomic Systems. arXiv.org. https://arxiv.org/abs/2312.07511

Good intuition on spherical harmonics