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Category: robotics/AI – Page 120

Crafting a unique and promising research hypothesis is a fundamental skill for any scientist. It can also be time consuming: New PhD candidates might spend the first year of their program trying to decide exactly what to explore in their experiments. What if artificial intelligence could help?

MIT researchers have created a way to autonomously generate and evaluate promising research hypotheses across fields, through human-AI collaboration. In a new paper, they describe how they used this framework to create evidence-driven hypotheses that align with unmet research needs in the field of biologically inspired materials.

Published Wednesday in Advanced Materials, the study was co-authored by Alireza Ghafarollahi, a postdoc in the Laboratory for Atomistic and Molecular Mechanics (LAMM), and Markus Buehler, the Jerry McAfee Professor in Engineering in MIT’s departments of Civil and Environmental Engineering and of Mechanical Engineering and director of LAMM.

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Research published in The American Journal of Human Genetics has identified a previously unknown genetic link to autism spectrum disorder (ASD). The study found that variants in the DDX53 gene contribute to ASD, providing new insights into the genetic underpinnings of the condition.

ASD, which affects more males than females, encompasses a group of neurodevelopmental conditions that result in challenges related to communication, social understanding and behavior. While DDX53, located on the X chromosome, is known to play a role in brain development and function, it was not previously definitively associated with autism.

In the study, researchers from The Hospital for Sick Children (SickKids) in Canada and the Istituto Giannina Gaslini in Italy clinically tested 10 individuals with ASD from eight different families and found that variants in the DDX53 gene were maternally inherited and present in these individuals. Notably, the majority were male, highlighting the gene’s potential role in the male predominance observed in ASD.

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To create cultured LMNs that replicate ALS neuron physiology and function, the Japanese team combined a small molecule-based approach with transcription factor transduction. The researchers achieved 80% induction efficiency of LMNs within just two weeks compared with conventional methods.

The resulting LMNs were found to have replicated ALS-specific pathologies, such as the abnormal aggregation of TDP-43 and FUS proteins. The team confirmed functionality of the LMNs using a multi-electrode array (MEA) system to measure firing activity and network activity, which were found to be similar to mature neurons.

Further analysis of the cultured LMNs showed that in addition to maintaining ALS cellular markers, the LMNs had reduced survival rates compared with healthy cells, mimicking ALS motor neuron responses.

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All 3D models created with Meshy AI
https://www.meshy.ai/?utm_source=youtube&utm_medium=fimcrux.

The sublime is an emotion described as equal parts awe and terror; a perfect description of our universe.

We created this short film concept showcasing a future vision of how humans might continue exploring that universe.

We used Meshy AI to generate all the 3D models in this film, like the ships, space probes and asteroids.

All the other VFX were created by us without the use of AI.

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Figuring out certain aspects of a material’s electron structure can take a lot out of a computer—up to a million CPU hours, in fact. A team of Yale researchers, though, are using a type of artificial intelligence to make these calculations much faster and more accurately. Among other benefits, this makes it much easier to discover new materials. Their results are published in Nature Communications.

In the field of materials science, exploring the of real materials is of particular interest, since it allows for better understanding of the physics of larger and more complex systems, such as moiré systems and defect states. Researchers typically will use a method known as density functional theory (DFT) to explore electronic structure, and for the most part it works fine.

“But the issue is that if you’re looking at excited state properties, like how materials behave when they interact with light or when they conduct electricity, then DFT really isn’t sufficient to understand the properties of the material,” said Prof. Diana Qiu, who led the study.

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Certain cells in the brain create a nurturing environment, enhancing the health and resilience of their neighbors, while others promote stress and damage. Using spatial transcriptomics and AI, researchers at Stanford’s Knight Initiative for Brain Resilience discovered these interactions playing out across the lifespan—suggesting local cellular interactions may significantly influence brain aging and resilience.

A new study was published in Nature in an article titled, “Spatial transcriptomic clocks reveal cell proximity effects in brain aging.”

“What was exciting to us was finding that some cells have a pro-aging effect on neighboring cells while others appear to have a rejuvenating effect on their neighbors,” said Anne Brunet, the Michele and Timothy Barakett Endowed Professor in Stanford’s department of genetics and co-senior investigator of the new study.

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Artificial intelligence (AI) is becoming increasingly useful for the prediction of emergency events such as heart attacks, natural disasters, and pipeline failures. This requires state-of-the-art technologies that can rapidly process data. In this regard, reservoir computing, specially designed for time-series data processing with low power consumption, is a promising option.

It can be implemented in various frameworks, among which physical reservoir computing (PRC) is the most popular. PRC with optoelectronic artificial synapses that mimic human synaptic elements are expected to have unparalleled recognition and real-time processing capabilities akin to the human visual system.

However, PRC based on existing self-powered optoelectronic synaptic devices cannot handle time-series data across multiple timescales, present in signals for monitoring infrastructure, natural environment, and health conditions.

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