I think we have to expand our thinking about the toroidal sphere even more. When looking at maps, I noticed the da Vinci map, from 1514, which uses the Reuleaux Triangle. This triangle is formed from 3 intersecting circles, and is in the center of a trefoil.

https://t.co/xvP6tVlIaE
Quite a surprise. Ferguson isn’t qualified to comment on such things. He lacks understanding of basic biological concepts, partly explaining why his predictions are often so extreme.

That said, I’ll take him as an ally. It’s clear we’re already at herd immunity. How can I be sure? London had a huge peak of excess deaths in spring. The timing of peak deaths showed clearly that the pandemic was self-limiting. Recall much debate about whether the interval...

...between lockdown & peak deaths was long enough for lockdown to have been the cause of reducing deaths? The answer was “no”. But it was close & I can understand people thinking “well, it’s close, so perhaps it was lockdown that did it?”, thereby establishing a foundational...

...myth favoured by those who genuinely believe lockdown saves lives (it clearly doesn’t).
I’ll get to London & here immunity. But those wavering about the evidence about lockdown in spring really will benefit from cogitating on this graph.

In blue are the 30 trusts with the earliest date of peak deaths in spring. In red, the 30 with the latest peak deaths. For me, this is proof that national lockdown, which began Mar 23, played no role whatsoever in turning the pandemic. You’ll see some areas had peak daily...

1/ #LBBB generates often a classical pattern on #EchoFirst. The pattern is very distinctive in Tissue Doppler of the septum.

The classical pattern arises from the time lapse of the activation and relaxation of the two walls, creating a pattern of interaction due to a sequence temporal imbalances of the tension between the two walls.

2/ As the septum is activated first, it contracts (shortening - septal flash) without activation of the lateral wall, which stretches. This generates slower pressure build up than a normal IVC, which then is prolonged.

3/ During ejection, the LV volume decreases, so both walls shorten. Tension, however, declines first in the septum, as this was activated first. This leads to a tension imbalance, so the lateral wall continues to shorten, the more relaxed septum stretches.

4/ The stretching of the septum builds up an elastic tension in the septum, which is released as recoil, when tension declines in the lateral wall, showing a post systolic shortening in the septum which is due to elasticity.

Today, we are bringing other exciting results involving black holes and AI. We released a new paper:

"Black Hole Weather Forecasting with Deep Learning: A Pilot Study"

Work by Roberta Duarte (@import_robs), Rodrigo Nemmen (@nemmen) and João Paulo Navarro (from @NVIDIABrasil).

The authors used deep learning to simulate the dynamics of gas accreting onto a black hole, i.e., black hole weather forecasting.

They trained the model (U-Net) with frames from numerical solutions of the hydrodynamical equations.

Numerical simulations are time-consuming. A simple simulation can take as long as 7 days to finish. If we go with more complex simulations, this time may increase.

We want to investigate if deep learning can be a new method to simulate accurately in less time!

In the paper, they discussed two examples:

1- The model simulating only one system after learning only from this system
2- The model simulating an unseen system after training with several systems with different initial conditions

In the first example, they trained the model with a single system and analyzed how the model simulates by iterative predictions.

The result is that the model can simulate up to 8e4 gravitational time accurately with a speed-up of 30000x faster!