TIL: bananas contain the primary compound in the honeybee’s “alarm” pheromone
Science helps us explain the phenomena. Turns out bananas contain a compound called isoamyl acetate (also known as isopentyl acetate) – the very same as that which is in honeybees’ alarm pheromone. Pure banana oil (used in emollients, perfumes, and to broaden the flavored milk range) is nothing but this colorless liquid ester, occasionally mixed with other chemicals. While bees’ alarm pheromone isn’t just isoamyl acetate – in fact there are over 40 compounds in the cocktail – it is the main active component. Guard bees, who patrol the entrance, and stinger bees, who comprise the militia, are the two castes within the hive most likely to release the pheromone. Both of these are worker bees (i.e. female) around 2-3 weeks old – the time it takes for their endocrine system to reach its prime. The scent – excreted from the Koschevnikov gland and other glands around the sting shaft – is released either when the bee pops out its stinger (like a cat retracting its claws), or goes full kamikaze and harpoons the mouse, robber bee or luckless human, rear-end first (inevitably dying in the assault). Having volatile properties, the ester evaporates and disperses rapidly from the origin point of the bee’s butt, making it suitable as a swift communication carrier. Once registered, it alerts the colony to the presence of an intruder or threat, lifting their aggro, and effectively coordinating an en masse defensive response. Any stray, lingering waft of a banana about you, then, will trigger a similar reaction (if slightly less intense). Don’t put too much faith in your smoker to avail you either.
(tags: bees honeybees science pheromones fruit bananas factoids)
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via the Tironian notes, a Roman shorthand syntax which originated the ‘Tironian et’ (?), Pompeii, and the Book of Kells (via Code Points)
(tags: ampersand characters via:codepoints history writing shorthand tironian-notes ciphers)
Google release an open-source differential-privacy lib
Differentially-private data analysis is a principled approach that enables organizations to learn from the majority of their data while simultaneously ensuring that those results do not allow any individual’s data to be distinguished or re-identified. This type of analysis can be implemented in a wide variety of ways and for many different purposes. For example, if you are a health researcher, you may want to compare the average amount of time patients remain admitted across various hospitals in order to determine if there are differences in care. Differential privacy is a high-assurance, analytic means of ensuring that use cases like this are addressed in a privacy-preserving manner. Currently, we provide algorithms to compute the following: Count Sum Mean Variance Standard deviation Order statistics (including min, max, and median)
(tags: analytics google ml privacy differential-privacy aggregation statistics obfuscation approximation algorithms)