Subject: Emerging Technologies

Why in the News?

Scientists at Fermilab in the USA have made an ultra-precise measurement of the muon’s magnetic behaviour, a finding that could hint at new physics beyond current laws.

Understanding Muon and G-2:

• Overview: A muon is a subatomic particle like an electron but about 200 times heavier.
• Behaviour: It has spin, making it act like a tiny magnet.
• g-Factor: The magnet’s strength is measured by the g-factor. In simple theory, g = 2, but quantum effects make it slightly different — this difference is called g-2 (g minus 2).
• Physics Relevance: Measuring g-2 can reveal unknown forces or particles beyond the Standard Model.

The Fermilab Breakthrough:

• Precision Record: Fermilab (USA) measured muon’s g-2 to an accuracy of 0.127 parts per million — comparable to detecting a 4-gram change on a 4-tonne elephant.
• Method: Muons were sent into a large magnetic ring, measuring the gap between spin rate and orbital rate.
• Results: Matches earlier experiments; disagreement with theory depends on calculation method used.
• Next Steps: Japan’s upcoming experiment will independently test results.

Significance:

• Potential New Physics: If the gap is real, it may signal undiscovered forces or particles.
• Refining Theory: If not, calculations will improve, sharpening known physics.
• Broader Impact: Advances precision science and deepens global understanding of fundamental physics.
• Lesson: Ultra-precise measurements can uncover hidden truths about nature.

Why in the News?

India has formally submitted a Letter of Request (LoR) to the United States seeking the co-production of Javelin anti-tank guided missiles (ATGMs) under the ‘Make in India’ initiative.

About the Javelin Missile:

• About: American-made man-portable anti-tank guided missile (ATGM).
• Developers: Jointly developed by Raytheon and Lockheed Martin.
• Purpose:
  • Defeats heavily armoured vehicles such as main battle tanks.
  • Effective against bunkers, fortifications, and helicopters.
• Operational Since: Entered service with the U.S. military in 1996.

Key Features:

• Range:
  • Standard effective range: 2.5 km.
  • Advanced variants: Can reach up to 4 km.
• Weight: Approximately 5.11 kg.
• Technology:
  • Employs a “fire-and-forget” system.
  • No need for operator guidance after launch.
• Target Engagement Modes:
  • Direct attack mode: For conventional engagement.
  • Top-attack mode: Hits the weaker top armour of tanks.
• Mobility & Safety:
  • Soldiers can relocate or reload immediately after launch.
  • Uses infrared guidance allowing quick post-launch cover.

Why in the News?

In a breakthrough, scientists have developed BioEmu—an AI tool that predicts the full range of protein shapes, offering faster and scalable insights into protein dynamics.

What is BioEmu?

• Overview: It is a deep learning tool that predicts the equilibrium ensemble of a protein — meaning all the different shapes a protein can naturally take.
• It works like diffusion models, starting with random/noisy inputs and learning to rebuild protein shapes.
• Training: It was trained on-
  • AlphaFold structures (millions of predicted proteins)
  • Molecular dynamics (MD) simulation data (200 ms)
  • 500,000 mutant sequences from lab experiments
• Speed: Once trained, BioEmu can quickly create thousands of 3D protein structures on a single GPU in just minutes to hours.

Key Features of BioEmu:

• Flexibility Capture: Shows how proteins change shape, fold/unfold, or form hidden binding pockets.
• Accuracy:
  • Detects 83% of large and 70–81% of small shape changes.
  • Predicts open/closed forms of enzymes like adenylate kinase.
• Mutation Analysis: Helps see how mutations affect protein structure and stability.
• Fast & Scalable: Much faster than traditional MD simulations; works on thousands of proteins using less computing power.
• Snapshots Only: Gives static 3D shapes, not full motion timelines.
• Limitations: Can’t handle membrane proteins, drug molecules, or multi-chain complexes.

Why is BioEmu Important?

• Improves Protein Modelling: Adds to what AlphaFold does by showing how proteins move, not just what they look like.
• Helps Drug Discovery: Finds hidden drug binding sites — speeding up the search for new medicines.
• More Accessible: Works on basic hardware, making advanced protein modelling available to more researchers.
• Combines with Other Tools: Can be used with molecular dynamics for deeper study.
• Future of Research: Encourages students and scientists to learn a mix of AI, biology, and physics.