Posts Tagged ‘from’

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.
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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

Diffusion Tutorial

March 1, 2025

Some tutorials on diffusion models:

[An Arxiv Tutorial]
https://arxiv.org/pdf/2403.18103
https://arxiv.org/abs/2403.18103

Chan, S. H. (2024, March 26). Tutorial on diffusion models for imaging and vision. arXiv.org. https://arxiv.org/abs/2403.18103

has master equation, forward & back SDE, relationship of SDE to p(x)

[Also, Some Useful Blogs]
https://baincapitalventures.notion.site/Diffusion-Without-Tears-14e1469584c180deb0a9ed9aa6ff7a4c https://yang-song.net/blog/2021/score/
https://lilianweng.github.io/posts/2021-07-11-diffusion-models/

tutorial on transformer

March 1, 2025

https://www.datacamp.com/tutorial/how-transformers-work

How Transformers Work: A Detailed Exploration of Transformer Architecture

Explore the architecture of Transformers, the models that have revolutionized data handling through self-attention mechanisms.

Jan 9, 2024 · 15 min read