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cost of privacy

March 14, 2026

game theory papers

https://www.science.org/doi/10.1126/sciadv.abe9986

Wan, Z., Vorobeychik, Y., Xia, W., Liu, Y., Wooders, M., Guo, J., Yin, Z., Clayton, E. W., Kantarcioglu, M., & Malin, B. A. (2021). Using game theory to thwart multistage privacy intrusions when sharing data. Science Advances, 7(50), eabe9986.
https://doi.org/10.1126/sciadv.abe9986

Guo, J., Clayton, E. W., Kantarcioglu, M., Vorobeychik, Y., Wooders, M., Wan, Z., Yin, Z., & Malin, B. A. (2023). A game theoretic approach to balance privacy risks and familial benefits. Scientific Reports, 13(1), 6932. https://doi.org/10.1038/s41598-023-33177-0

They seem to be more focused on the cost to the attacker

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Human hippocampal neurogenesis in adulthood, ageing and Alzheimer’s disease

March 1, 2026

Interesting paper on the Aging Brain. Featured in NY Times.

Nature https://www.nature.com/articles/s41586-026-10169-4

Disouky, A., Sanborn, M. A., Sabitha, K. R., Mostafa, M. M., Ayala, I. A., Bennett, D. A., Lu, Y., Zhou, Y., Keene, C. D., Weintraub, S., Gefen, T., Mesulam, M., Geula, C., Maienschein-Cline, M., Rehman, J., & Lazarov, O. (2026). Human hippocampal neurogenesis in adulthood, ageing and Alzheimer’s disease. Nature.
https://doi.org/10.1038/s41586-026-10169-4

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Huntington disease | Nature Reviews Disease Primers

February 22, 2026

https://www.nature.com/articles/nrdp20155

nrdp20155.pdf

Bates, G. P., Dorsey, R., Gusella, J. F., Hayden, M. R., Kay, C., Leavitt, B. R., Nance, M., Ross, C. A., Scahill, R. I., Wetzel, R., Wild, E. J., & Tabrizi, S. J. (2015). Huntington disease. Nature Reviews Disease Primers, 1(1), 15005.
https://doi.org/10.1038/nrdp.2015.5

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Toward practical high-capacity low-maintenance storage of digital information in synthesised DNA – PMC

February 15, 2026

https://pmc.ncbi.nlm.nih.gov/articles/PMC3672958/

Goldman, N., Bertone, P., Chen, S., Dessimoz, C., LeProust, E. M., Sipos, B., & Birney, E. (2013). Towards practical, high-capacity, low-maintenance information storage in synthesized DNA. Nature, 494(7435), 77–80. https://doi.org/10.1038/nature11875

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Nature medcine “A minimally invasive dried blood spot biomarker test for the detection of Alzheimer’s dis ease pathology”

January 26, 2026

QT:{{”
The DROP-AD project investigates the potential of dried plasma spot (DPS) and dried blood spot (DBS) analysis, derived from capillary blood, for detecting AD biomarkers, including phosphorylated tau at amino acid 217 (p-tau217), glial fibrillary acidic protein and neurofilament light. …. Similarly, we demonstrated the successful detection of glial fibrillary acidic protein and neurofilament light with strong correlations between DBS and DPS, respectively, using paired venous plasma samples.
“}}

Might find this paper very interesting. Just published this month in Nature Medicine. “A minimally invasive dried blood spot biomarker test for the detection of Alzheimer’s disease pathology.”

A minimally invasive dried blood spot biomarker test for the detection of Alzheimer’s disease pathology – Nature Medicine
https://www.nature.com/articles/s41591-025-04080-0

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Xpresso

January 25, 2026

https://xpresso.gs.washington.edu/

Agarwal V, Shendure J. Predicting mRNA abundance directly from genomic sequence using deep convolutional neural networks. 2020. Cell Reports 31 (7), 107663.

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Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation | Nature Genetics

January 25, 2026

https://github.com/calico/borzoi

N&V – https://www.nature.com/articles/s41588-025-02154-w

https://www.nature.com/articles/s41588-024-02053-6#:~:text=This%20paper%20proposes%20a%20new%20machine%2Dlearning%20model%2C,that%20drive%20RNA%20expression%20and%20post%2Dtranscriptional%20regulation QT:{{” ere, we introduce Borzoi, a model that learns to predict cell-type-specific and tissue-specific RNA-seq coverage from DNA sequence. “}}

Linder, J., Srivastava, D., Yuan, H., Agarwal, V., & Kelley, D. R. (2025). Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation. Nature Genetics, 57(4), 949–961.
https://doi.org/10.1038/s41588-024-02053-6

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Predicting cell type-specific epigenomic profiles accounting for distal genetic effects | Nature Communications

January 25, 2026

https://www.nature.com/articles/s41467-024-54441-5
QT:{{” Enformer Celltyping can predict in new cell types, imputing their epigenetic signal, by embedding global and local chromatin accessibility (ATAC-Seq) signals for the cell type of interest. “}}

Murphy, A. E., Beardall, W., Rei, M., Phuycharoen, M., & Skene, N. G. (2024). Predicting cell type-specific epigenomic profiles accounting for distal genetic effects. Nature Communications, 15(1), 9951. https://doi.org/10.1038/s41467-024-54441-5

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From R.A. Fisher’s 1918 Paper to GWAS a Century Later | Genetics | Oxford Academic

January 1, 2026

https://academic.oup.com/genetics/article/211/4/1125/5931511

Visscher, P. M., & Goddard, M. E. (2019). From R.A. Fisher’s 1918 paper to GWAS a century later. Genetics, 211(4), 1125–1130.
https://doi.org/10.1534/genetics.118.301594

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The human disease network | PNAS

January 1, 2026

https://www.pnas.org/doi/10.1073/pnas.0701361104?url_ver=Z39.88-2003&rfr_id=ori%3Arid%3Acrossref.org&rfr_dat=cr_pub++0pubmed QT:{{” We find that essential human genes are likely to encode hub proteins and are expressed widely in most tissues. This suggests that disease genes also would play a central role in the human interactome. In contrast, we find that the vast majority of disease genes are nonessential and show no tendency to encode hub proteins, and their expression pattern indicates that they are localized in the functional periphery of the network. “}}

Goh, K., Cusick, M. E., Valle, D., Childs, B., Vidal, M., & Barabási, A. (2007). The human disease network. Proceedings of the National Academy of Sciences, 104(21), 8685–8690.
https://doi.org/10.1073/pnas.0701361104

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