Today we report the use of base editing in patient-derived cells and in mice to correct the most common cause of progeria, the devastating rapid aging disease. Progeria is typically caused by a dominant negative C•G-to-T•A point mutation in LMNA. 1/11 https://t.co/O7dkEYpndg

Thread for folks looking for roles in biopharma VC. Nearly every week I speak to someone who wants to get into biopharma VC. One high-level theme: even for entry roles, you *generally* need to check the box “understands science” as well as the box “can do basic financial... 1/9

🤫

The distinction of measuring the time in a scientific manner is achieved only by Bhāratīyas. Time has been measured from the smallest dimension to infiniteness. Even Time is envisioned as God and venerated. Paramēśvara was extolled as ‘Mahākāla’, the very embodiment of Time.

Nārāyaṇa Upaniṣad envisages Nārāyaṇa as ‘Kālaśca Nārāyaṇa:’, the very embodiment of Time, ages ago. The measurement of Time by Bhāratīyas is as ancient as the very God Himself. Count of many future ages has also been done even from very ancient times.

Since these calculations aren't dependent upon the birth & death of any individual, but are done based on astronomical science, they are thoroughly scientific. Unfortunately, during the British rule, the Bhāratīya way of Time measurement was casted as an illusion...

...with an evil intention to establish their command. Though it is 70+ years since independence, blindly time is still measured as BC & AD or CE in this religion-less land without even making any attempt to comprehend the magnanimity of Bhāratīya Time measurement methods.

Time measurement based on inhalation and exhalation : (image)

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!