机器学习在太空飞行研究中的应用

NASA's Ames Research Center has conducted a study using machine learning to investigate the molecular mechanisms behind muscle atrophy in spaceflight. The research, published in the journal npj Microgravity, focused on the alterations in calcium uptake in muscles when exposed to microgravity. By applying machine learning methods, the study identified patterns in datasets from mice exposed to microgravity, shedding light on the physiological changes in the calcium channel sarcoplasmic/endoplasmic reticulum (SERCA) pump, which lead to muscle loss in spaceflight rodents.

The study also aimed to identify biomarkers that could inform innovative countermeasures against muscle atrophy in space. Through machine learning analysis, the researchers identified specific proteins, Acyp1 and Rps7, as predictive biomarkers associated with enhanced calcium intake in fast-twitch muscles. This approach offers a first look at the use of machine learning to understand calcium uptake in muscle under microgravity conditions and demonstrates the role of NASA's open science initiative in accelerating space biology research.

Furthermore, the study showcased the collaborative efforts of an international research team from the U.S., Canada, Denmark, and Australia, highlighting the involvement of an undergraduate from UC Berkeley as the article's first author. This underscores the potential for collaborations between NASA and Berkeley in life sciences research, particularly with the upcoming Berkeley Space Center at NASA Research Park.