The complex structure of the heart leads to significant challenges in echocardiography, especially in acquisition cardiac ultrasound images. Successful echocardiography requires a thorough understanding of the structures on the two-dimensional plane and the spatial relationships between planes in three-dimensional space. In this paper, we innovatively propose a large-scale self-supervised pre-training method to acquire a cardiac structure-aware world model. The core innovation lies in constructing a self-supervised task that requires structural inference by predicting masked structures on a 2D plane and imagining another plane based on pose transformation in 3D space. To support large-scale pre-training, we collected over 1.36 million echocardiograms from ten standard views, along with their 3D spatial poses. In the downstream probe guidance task, we demonstrate that our pre-trained model consistently reduces guidance errors across the ten most common standard views on the test set with 0.29 million samples from 74 routine clinical scans, indicating that structure-aware pre-training benefits the scanning.

That headline might seem a bit churlish, given the tremendous amount of energy, investment, and hype in the AI space, as well as undeniable evidence of technological progress. After all, AI today can beat any human in games ranging from chess to Starcraft (DeepMind's AlphaZero and AlphaStar); it can write a B- college history essay in seconds with a few prompts (OpenAI's GPT-3); it can draw on-demand illustrations of surprising creativity and quality (OpenAI's DALL-E 2). For AI proponents like Sam Altman, OpenAI's CEO, these advances herald an era where "AI creative tools are going to be the biggest impact on creative work flows since the computer itself," as he tweeted last month. That may turn out to be true. But in the here and now, I'm still left somewhat underwhelmed.

For the function of many biomolecules, their three-dimensional structure is crucial. Researchers are therefore not only interested in the sequence of the individual building blocks of biomolecules, but also in their spatial structure. With the help of artificial intelligence (AI), bioinformaticians can already reliably predict the three-dimensional structure of a protein from its amino acid sequence. For RNA molecules, however, this technology is still in its infancy. Researchers at Ruhr-Universität Bochum (RUB) describe a way to use AI to reliably predict the structure of certain RNA molecules from their nucleotide sequence in the journal PLOS Computational Biology on July 7, 2022.

The tech media outlet Toolbox featured the views of 10 experts on "How will AI evolve in the next year?" Edge technology that experts should pay attention to next year was also intensively discussed. The first place was occupied by MIT's Neil Thompson research team featuring an article on the cost of energy to train deep learning systems. As a result of analyzing the improvements of the image classifier, the research team found that "to cut the error rate in half, it can be expected that 500 times more computational resources are required." "The rising cost requires researchers to devise more efficient ways to solve these problems, otherwise we will give up research on these problems, and progress will be difficult," he said.

Artificial intelligence recently achieved the breakthrough of predicting the three-dimensional structures of proteins. The next frontier is presented by intrinsically disordered proteins (IDPs), which, representing 30% to 50% of proteomes, readily access vast conformational space. Molecular dynamics (MD) simulations are promising in sampling IDP conformations, but only at extremely high computational cost. Here, we developed generative autoencoders that learn from short MD simulations and generate full conformational ensembles. An encoder represents IDP conformations as vectors in a reduced-dimensional latent space. The mean vector and covariance matrix of the training dataset are calculated to define a multivariate Gaussian distribution, from which vectors are sampled and fed to a decoder to generate new conformations. The ensembles of generated conformations cover those sampled by long MD simulations and are validated by small-angle X-ray scattering profile and NMR chemical shifts. This work illustrates the vast potential of artificial intelligence in conformational mining of IDPs. Generative autoencoders create full conformational ensembles of intrinsically disordered proteins from short molecular dynamics simulations.

Artificial intelligence has changed the way science is done by allowing researchers to analyze the massive amounts of data modern scientific instruments generate. It can find a needle in a million haystacks of information and, using deep learning, it can learn from the data itself. AI is accelerating advances in gene hunting, medicine, drug design and the creation of organic compounds. Deep learning uses algorithms, often neural networks that are trained on large amounts of data, to extract information from new data. It is very different from traditional computing with its step-by-step instructions.

DeepMind's AlphaFold represents the first time a significant scientific problem has been solved by ... [ ] AI. It can be difficult to distinguish between substance and hype in the field of artificial intelligence. In order to stay grounded, it is important to step back from time to time and ask a simple question: what has AI actually accomplished or enabled that makes a difference in the real world? This summer, DeepMind delivered the strongest answer yet to that question in the decades-long history of AI research: AlphaFold, a software platform that will revolutionize our understanding of biology. In 1972, in his acceptance speech for the Nobel Prize in Chemistry, Christian Anfinsen made a historic prediction: it should in principle be possible to determine a protein's three-dimensional shape based solely on the one-dimensional string of molecules that comprise it. Finding a solution to this puzzle, known as the "protein folding problem," has stood as a grand challenge in the field of biology for half a century.

Today's commercial aircraft are typically manufactured in sections, often in different locations -- wings at one factory, fuselage sections at another, tail components somewhere else -- and then flown to a central plant in huge cargo planes for final assembly. But what if the final assembly was the only assembly, with the whole plane built out of a large array of tiny identical pieces, all put together by an army of tiny robots? That's the vision that graduate student Benjamin Jenett, working with Professor Neil Gershenfeld in MIT's Center for Bits and Atoms (CBA), has been pursuing as his doctoral thesis work. It's now reached the point that prototype versions of such robots can assemble small structures and even work together as a team to build up a larger assemblies. The new work appears in the October issue of the IEEE Robotics and Automation Letters, in a paper by Jenett, Gershenfeld, fellow graduate student Amira Abdel-Rahman, and CBA alumnus Kenneth Cheung SM '07, PhD '12, who is now at NASA's Ames Research Center, where he leads the ARMADAS project to design a lunar base that could be built with robotic assembly.

Today's commercial aircraft are typically manufactured in sections, often in different locations -- wings at one factory, fuselage sections at another, tail components somewhere else -- and then flown to a central plant in huge cargo planes for final assembly. But what if the final assembly was the only assembly, with the whole plane built out of a large array of tiny identical pieces, all put together by an army of tiny robots? That's the vision that graduate student Benjamin Jenett, working with Professor Neil Gershenfeld in MIT's Center for Bits and Atoms (CBA), has been pursuing as his doctoral thesis work. It's now reached the point that prototype versions of such robots can assemble small structures and even work together as a team to build up a larger assemblies. The new work appears in the October issue of the IEEE Robotics and Automation Letters, in a paper by Jenett, Gershenfeld, fellow graduate student Amira Abdel-Rahman, and CBA alumnus Kenneth Cheung SM '07, PhD '12, who is now at NASA's Ames Research Center, where he leads the ARMADAS project to design a lunar base that could be built with robotic assembly.

Researchers at MIT created an app to turn proteins into music, to access complicated protein information in a new way. Based on these protein-derived musical pieces, they trained neural networks to create new music, which could then be turned into protein structures. MIT researchers developed an app that can turn a protein's amino acid sequence into music. More than just an amusing activity, this music can train neural networks to create entirely new proteins. Understanding the exact correlation between protein structure and function is an ongoing research question in biochemistry.