"NO LONGER BEST IN THE WORLD"
UNEP's new Human Development Index includes a new (separate) index: Planetary pressures-adjusted HDI (PHDI). News in Norway is that its position drops from #1 to #16 because of this, while Ireland rises from #2 to #1.
Why?

https://t.co/aVraIEzRfh

So proud of my @UNGlobalPulse team and collaborators that step by step and over the years have managed to bring 🤖 science closer to humanitarian and development work. We now have a library!! 📚 📚📚 https://t.co/rOxXQRllo5 Some peer-reviewed highlights in this thread 👇

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Get a cup of coffee.

In this thread, we'll cover *State Quantities* and *Flow Quantities*.

This mental model can help us analyze many different entities -- from complex engineering systems to publicly traded companies.

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A "State Quantity" is something that's associated with a *specific point in time*.

For example, the number of followers a Twitter account has is a state quantity. This number can go up and down over time. But at any one *specific* time point, it's fixed.

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Similarly, the amount of cash a company has is a state quantity.

This cash balance can also rise and fall over time.

But pick any one *specific* time point -- and the company can only have one *specific* cash balance at that time point.

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By contrast, a "Flow Quantity" is something that's associated with a time *interval* rather than a time *point*.

For example, the number of followers gained by a Twitter account is a flow quantity. It's meaningful only when we specify a time interval (eg, the last 30 days).

5/

A simple analogy is that a State Quantity is like a photo -- a snapshot at a particular *point* in time.

But a Flow Quantity is like a video -- a record of events that take place during a particular time *interval*.

The number 666 relates to the carbon atom, and man. Carbon-12; one of 5 elements in the human DNA is composed of 6 protons, 6 electrons and 6 neutrons, which equates to 666. 
.@realDonaldTrump
.@GenFlynn
.@RudyGiuliani

Did we get dietary saturated fats all wrong? The #HADLmodel provides a new understanding and an opportunity to get it right. THREAD👇👇👇
@simondankel @kariannesve

Increased dietary saturated fatty acids lead to increased cholesterol in lipoproteins, but we don’t know why. Enter the #HADLmodel, which explains changes in lipoprotein cholesterol as adaptive homeostatic adjustments that ensure optimal cell membrane fluidity and cell function.

We propose that circulating lipoproteins enable appropriate redistribution of cholesterol molecules between specific cells and tissues, to accomodate changes in dietary fatty acid supply, due to our omnivore nature and variable intake of fatty acids. #HADLmodel

Our #HADLmodel implies that circulating levels of LDL change for protective, not for pathological reasons; an SFA-induced raise in LDL cholesterol in healthy individuals is a normal response, while a lack of this needed response may reflect a deeper pathology in lipid handling.

Circulating lipoproteins may change for pathological reasons, when regulatory mechanisms become disrupted by pathogenic processes related e.g. to inflammatory processes. Diverging lipoprotein responses in healthy versus metabolically unhealthy individuals support this view.