https://genome.cshlp.org/content/9/7/629.full
Alu implicated in duplication related to 3-color vision
https://genome.cshlp.org/content/9/7/629.full
Alu implicated in duplication related to 3-color vision
https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1000095
reawakened pseudogenes useful against HIV
QT:{{”
A stronger, cooler wood
One good way to reduce the amount of cooling a building needs is to make sure it reflects away infrared radiation. Passive radiative cooling materials are engineered to do this extremely well. Li et al. engineered a wood through delignification and re-pressing to create a mechanically strong material that also cools passively. They modeled the cooling savings of their wood for 16 different U.S. cities, which suggested savings between 20 and 50%. Cooling wood would be of particular value in hot and dry climates.
“}}
interesting material for anti-insulation
https://science.sciencemag.org/content/364/6442/760.full
https://science.sciencemag.org/content/364/6442/760.editor-summary
https://science.sciencemag.org/content/366/6464/447.full
data access issues
There’s a paper on this topic that introduced the idea of “kind and wicked learning environments”:
https://pdfs.semanticscholar.org/5c5d/33b858eaf38f6a14b3f042202f1f44e04326.pdf
…in wicked environments it is difficult to do inference based on data. One solution seems to be to break down the problem in such a way that you can observe sub-problems in a kind environment.
The Two Settings of Kind and Wicked Learning Environments
Robin M. Hogarth1, Tomás Lejarraga2, and Emre Soyer3
Abstract
QT:{{” Inference involves two settings: In the first, information is acquired (learning); in the second, it is applied (predictions or choices). Kind learning environments involve close matches between the informational elements in the two settings and are a necessary condition for accurate inferences. Wicked learning environments involve mismatches. This conceptual framework facilitates identifying sources of inferential errors and can be used, among other things, to suggest how to target corrective procedures. For example, structuring learning environments to be kind improves probabilistic judgments. Potentially, it could also enable economic agents to exhibit maximizing behavior.
“}}
https://science.sciencemag.org/content/early/2020/03/30/science.abb6936
Great idea for #covid19… However, digital contact tracing has serious #privacy issues that have to be considered and perhaps ameliorated.
Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing
Luca Ferretti1,*, Chris Wymant1,*, Michelle Kendall1, Lele Zhao1, Anel Nurtay1, Lucie Abeler-Dörner1, Michael Parker2, David Bonsall1,3,†, Christophe Fraser1,4,†,‡
Science 31 Mar 2020: eabb6936
DOI: 10.1126/science.abb6936
Fig 2 shows the breakdown of infections into four types:
pre-symptomatic, symptomatic, environmental, and asymptomatic, and they contribute 0.9, 0.8., 0.2, and 0.1 each to the basic reproduction number of 2. To stop the spread of infections, you need to take measures to get the area under the curve to below 1. The paper then shows that when you do quarantining and contact tracing, you can’t get the reproduction number below 1. However, if you improve the speed of quarantining and contact tracing with a digital app/centralized system, then you can get it below 1.
Adrian Baez-Ortega1, Kevin Gori1,*, Andrea Strakova1,*, Janice L. Allen2, Karen M. Allum3, Leontine Bansse-Issa4, …. Michael R. Stratton62, Ludmil B. Alexandrov63, Iñigo Martincorena62, Elizabeth P. Murchison1,†
Science 02 Aug 2019:
Vol. 365, Issue 6452, eaau9923
DOI: 10.1126/science.aau9923
Accurate estimation of cell composition in bulk expression through robust integration of single-cell information
Brandon Jew, Marcus Alvarez, Elior Rahmani, Zong Miao, Arthur Ko, Jae Hoon Sul, Kirsi H. Pietiläinen, Päivi Pajukanta, Eran Halperin doi:
newly published tool
TADsplimer reveals splits and mergers of topologically associating domains for epigenetic regulation of transcription
Guangyu Wang, Qingshu Meng, Bo Xia, Shuo Zhang, Jie Lv, Dongyu Zhao, Yanqiang Li, Xin Wang, Lili Zhang, John P. Cooke, Qi Cao & Kaifu Chen
https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-01992-7