https://en.wikipedia.org/wiki/E._coli_long-term_evolution_experiment QT:{{” It has been tracking genetic changes in 12 initially identical populations of asexual Escherichia coli bacteria since 24 February 1988.[4] Lenski performed the 10,000th transfer of the experiment on March 13, 2017.[5] The populations reached over 73,000 generations in early 2020, shortly before being frozen because of the COVID-19 pandemic.[6] “}}
Archive for the '–' Category
E. coli long-term evolution experiment – Wikipedia
December 26, 2025Exaptation – Wikipedia
December 26, 2025https://en.wikipedia.org/wiki/Exaptation
QT:{{” Exaptation or co-option is a shift in the function of a trait during evolution. For example, a trait can evolve because it served one particular function, but subsequently it may come to serve another. Exaptations are common in both anatomy and behaviour. Bird feathers are a classic example. Initially they may have evolved for temperature regulation, but later were adapted for flight. When feathers were first used to aid in flight, that was an exaptive use. “}}
Syncytin-1 – Wikipedia
December 26, 2025https://en.wikipedia.org/wiki/Syncytin-1
QT:{{” Syncytin-1 also known as enverin is a protein found in humans and other primates that is encoded by the ERVW-1 gene (endogenous retrovirus group W envelope member 1). Syncytin-1 is a cell-cell fusion protein whose function is best characterized in placental development.[3][4] The placenta in turn aids in embryo attachment to the uterus and establishment of a nutrient supply. “}}
Epistasis
December 26, 2025https://www.genome.gov/genetics-glossary/Epistasis#:~:text=Epistasis%20is%20a%20circumstance%20where,one%20or%20more%20other%20genes. QT:{{”
Epistasis is a circumstance where the expression of one gene is modified (e.g., masked, inhibited or suppressed) by the expression of one or more other genes. “}}
Unlocking AI: Visual Question Answering Insights
December 26, 2025https://viso.ai/deep-learning/understanding-visual-question-answering-vqa/ QT:{{”
What is Visual Question Answering (VQA)?
The simplest way of defining a VQA system is a system capable of answering questions related to an image. It takes an image and a text-based question as inputs and generates the answer as output. The nature of the problem defines the nature of the input and output of a VQA model.
Inputs may include static images, videos with audio, or even infographics. Questions can be presented within the visual or asked separately regarding the visual input. It can answer multiple-choice questions, YES/NO (binary questions), or any open-ended questions about the provided input image. It allows a computer program to understand and respond to visual and textual input in a human-like manner.
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Edge.org
December 26, 2025https://www.edge.org/response-detail/27082
QT:{{” Toss a mouse from a building. It will land, shake itself off and scamper away. But if similarly dropped, “… a rat is killed, a man is broken, a horse splashes.” So wrote J.B.S. Haldane in his 1926 essay “On Being the Right Size.” Size matters, but not in the way a city-stomping Godzilla or King Kong might hope.
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Ada Lovelace – Wikipedia
December 26, 2025https://en.wikipedia.org/wiki/Ada_Lovelace
QT:{{” From 1832, when she was seventeen, her mathematical abilities began to emerge,[23] and her interest in mathematics dominated the majority of her adult life.[39] Her mother’s obsession with rooting out any of the insanity of which she accused Byron was one of the reasons that Ada was taught mathematics from an early age. She was privately educated in mathematics and science by William Frend, William King,[a] and Mary Somerville, the noted 19th-century researcher and scientific author. In the 1840s, the mathematician Augustus De Morgan extended her “much help in her mathematical studies” including study of advanced calculus topics including the “numbers of Bernoulli” (that formed her celebrated algorithm for Babbage’s Analytical Engine).[40] In a letter to Lady Byron, De Morgan suggested that Ada’s skill in mathematics might lead her to become “an original mathematical investigator, perhaps of first-rate
eminence”.[41] “}}
Protein moonlighting – Wikipedia
December 26, 2025https://en.wikipedia.org/wiki/Protein_moonlighting
QT:{{”
Histone H3 – DNA packaging – Copper reductase
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The social and structural architecture of the yeast protein interactome – PubMed
December 26, 2025https://pubmed.ncbi.nlm.nih.gov/37968396/
QT:{{”
…most yeast proteins interact with at least sixteen others. The highly organised yeast interactome includes 3,927 proteins linked by 31,004 interactions….Much like human social networks (such as Facebook), the average shortest path between any two proteins in yeast involves just four interactions. So while most protein ‘nodes’ are not directly connected, there is on average just four degrees of separation between them. This organisation, characterised by local clustering and relatively short average path lengths between nodes, is known as a small-world network.
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from WOOLFSON et al. (’26)
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