Archive for the 'SciLit' Category

The International Human Epigenome Consortium: A Blueprint for Scientific Collaboration and Discovery: Cell

October 18, 2025

Capstone reviews/perspectives for reference

**IHEC**

The International Human Epigenome Consortium: A Blueprint for Scientific Collaboration and Discovery
Hendrik G. Stunnenberg ∙ The International Human Epigenome Consortium4 ∙ Martin Hirst

Stunnenberg, H. G., Hirst, M., Abrignani, S., Adams, D., De Almeida, M., Altucci, L., Amin, V., Amit, I., Antonarakis, S. E., Aparicio, S., Arima, T., Arrigoni, L., Arts, R., Asnafi, V., Esteller, M., Bae, J., Bassler, K., Beck, S., Berkman, B., . . . Zipprich, G. (2016). The International Human Epigenome Consortium: a blueprint for Scientific collaboration and Discovery. Cell, 167(5), 1145–1149.
https://doi.org/10.1016/j.cell.2016.11.007

** EXRNA**

The Extracellular RNA Communication Consortium: Establishing Foundational Knowledge and Technologies for Extracellular RNA Research

Das, S., Ansel, K. M., Bitzer, M., Breakefield, X. O., Charest, A., Galas, D. J., Gerstein, M. B., Gupta, M., Milosavljevic, A., McManus, M. T., Patel, T., Raffai, R. L., Rozowsky, J., Roth, M. E., Saugstad, J. A., Van Keuren-Jensen, K., Weaver, A. M., Laurent, L. C., Abdel-Mageed, A. B., . . . Zhang, H. (2019). The Extracellular RNA Communication Consortium: Establishing foundational knowledge and technologies for extracellular RNA research. Cell, 177(2), 231–242. https://doi.org/10.1016/j.cell.2019.03.023

**ENCODE3**

Perspectives on ENCODE
The ENCODE Project Consortium, Michael P Snyder 1,2,✉, Thomas R Gingeras 3, Jill E Moore 4, Zhiping Weng 4,5,6, Mark B Gerstein 7, Bing Ren 8,9, Ross C Hardison 10, John A Stamatoyannopoulos 11,12,13, Brenton R Graveley 14, Elise A Feingold 15, Michael J Pazin 15, Michael Pagan 15, Daniel A Gilchrist 15, Benjamin C Hitz 1, J Michael Cherry 1, Bradley E Bernstein 16, Eric M Mendenhall 17,18, Daniel R Zerbino 19, Adam Frankish 19, Paul Flicek 19, Richard M Myers 18

Abascal, F., Acosta, R., Addleman, N. J., Adrian, J., Afzal, V., Aken, B., Ai, R., Akiyama, J. A., Jammal, O. A., Amrhein, H., Anderson, S. M., Andrews, G. R., Antoshechkin, I., Ardlie, K. G., Armstrong, J., Astley, M., Banerjee, B., Barkal, A. A., Barnes, I. H. A., . . . Myers, R. M. (2020).

Perspectives on ENCODE. Nature, 583(7818), 693–698.
https://doi.org/10.1038/s41586-020-2449-8

A computational pipeline for spatial mechano-transcriptomics | Nature Methods

September 7, 2025

https://www.nature.com/articles/s41592-025-02618-1

Hallou, A., He, R., Simons, B. D., & Dumitrascu, B. (2025). A computational pipeline for spatial mechano-transcriptomics. Nature Methods. https://doi.org/10.1038/s41592-025-02618-1

Reviews:
https://www.nature.com/articles/s41580-023-00583-1#Sec35
(difficult to follow)

Combining with Spatial transcriptomics:
https://www.nature.com/articles/s41592-025-02618-1
(new thing)

Human exposure to PM10 microplastics in indoor air | PLOS One

July 30, 2025

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0328011

Yakovenko, N., Pérez-Serrano, L., Segur, T., Hagelskjaer, O., Margenat, H., Roux, G. L., & Sonke, J. E. (2025). Human exposure to PM10 microplastics in indoor air. PLOS One.
https://doi.org/10.1371/journal.pone.0328011

Gene name errors are widespread in the scientific literature | Genome Biology | Full Text

July 26, 2025

https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-1044-7

Ziemann, M., Eren, Y., & El-Osta, A. (2016). Gene name errors are widespread in the scientific literature. Genome Biology, 17(1). https://doi.org/10.1186/s13059-016-1044-7

Scalable emulation of protein equilibrium ensembles with generative deep learning | Science

July 12, 2025

https://www.science.org/doi/10.1126/science.adv9817

Lewis, S., Hempel, T., Jiménez-Luna, J., Gastegger, M., Xie, Y., Foong, A. Y. K., Satorras, V. G., Abdin, O., Veeling, B. S., Zaporozhets, I., Chen, Y., Yang, S., Foster, A. E., Schneuing, A., Nigam, J., Barbero, F., Stimper, V., Campbell, A., Yim, J., . . . Noé, F. (2025, July 10). Scalable emulation of protein equilibrium ensembles with generative deep learning. Science.
https://www.science.org/doi/10.1126/science.adv9817

Aβ∗56 is a stable oligomer that impairs memory function in mice – PMC

June 26, 2025

https://pmc.ncbi.nlm.nih.gov/articles/PMC10905009/

Liu, P., Lapcinski, I. P., Hlynialuk, C. J., Steuer, E. L., Loude, T. J., Shapiro, S. L., Kemper, L. J., & Ashe, K. H. (2024). Aβ∗56 is a stable oligomer that impairs memory function in mice. iScience, 27(3), 109239. https://doi.org/10.1016/j.isci.2024.109239

Aβ∗56 is a ∼56-kDa, SDS-stable, A11-reactive, non-plaque-dependent, water-soluble, brain-derived oligomer containing canonical Aβ(1-40).

Practical guide to SHAP analysis: Explaining supervised machine learning model predictions in drug development – PMC

June 10, 2025

https://pmc.ncbi.nlm.nih.gov/articles/PMC11513550/

Ponce‐Bobadilla, A. V., Schmitt, V., Maier, C. S., Mensing, S., & Stodtmann, S. (2024). Practical guide to SHAP analysis: Explaining supervised machine learning model predictions in drug development. Clinical and Translational Science, 17(11).
https://doi.org/10.1111/cts.70056

Mastering diverse control tasks through world models | Nature

April 26, 2025

https://www.nature.com/articles/s41586-025-08744-2

Hafner, D., Pasukonis, J., Ba, J., & Lillicrap, T. (2025). Mastering diverse control tasks through world models. Nature.
https://doi.org/10.1038/s41586-025-08744-2

Microglia: Immune and non-immune functions – ScienceDirect

April 6, 2025

https://www.sciencedirect.com/science/article/pii/S107476132100399X

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.
“}}