AI Catalyzes Gene Activation Research, Uncovers Rare DNA Sequences

More amazement via AI. 

AI Catalyzes Gene Activation Research, Uncovers Rare DNA Sequences

By UC San Diego Today,  May 25, 2023

Investigating DNA sequences.

Using machine learning, the researchers discovered the downstream core promoter region (DPR), a “gateway” DNA activation code that’s involved in the operation of up to a third of our genes.

The University of California, San Diego (UCSD)'s James T. Kadonaga and colleagues have used artificial intelligence (AI) to advance gene activation research by identifying "synthetic extreme" DNA sequences.

The researchers trained machine learning models on 200,000 established DNA sequences, then tested 50 million DNA sequences with the models to compare downstream core promoter region (DPR) gene activation elements in humans and fruit flies, exposing custom-tailored DPR sequences specific to both species.

Said Kadonaga, "There are countless practical applications of this AI-based approach. The synthetic extreme DNA sequences might be very rare, perhaps one-in-a-million—if they exist they could be found by using AI."

From UC San Diego Today

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Wearable Sensor Provides Cardiac Imaging on the Go

I have a similar implanted device, but without the imaging and other smarts detailed here.  

Wearable Sensor Provides Cardiac Imaging on the Go

By UC San Diego Today, February 3, 2023

The unique design of the sensor makes it ideal for bodies in motion.

The wearable uses ultrasound to continuously capture images of the four chambers of the heart at different angles, and analyze a clinically relevant subset of the images in real time using custom-built AI technology.

A team led by researchers at the University of California, San Diego (UC San Diego) has developed a portable ultrasound device that uses custom algorithms to measure how much blood is being pumped by the heart.

The postage-stamp-sized patch captures images of the heart at different angles using ultrasound.

The technology performs real-time analysis of a subset of the images, which have high spatial resolution, temporal resolution, and contrast.

UC San Diego's Ruixiang Qi said the device provides "accurate and continuous waveforms of key cardiac indices in different physical states, including static and after exercise, which has never been achieved before."

From UC San Diego Today   View Full Article  

Predictive Database for Materials

Materials science becoming increasingly important

 Nanoengineers Develop Predictive Database for Materials

UC San Diego Today, Emerson Dameron, November 28, 2022

The M3GNet algorithm developed by nanoengineers at the University of California, San Diego (UCSD)'s Jacobs School of Engineering can forecast the structure and dynamic properties of any material almost instantaneously. Researchers used M3GNet to compile the matterverse.ai database of more than than 31 million yet-to-be-synthesized materials with traits predicted by machine learning algorithms. UCSD's Shyue Ping Ong and colleagues combined graph neural networks with many-body interactions into a highly accurate deep learning framework that operates across the entire periodic table. The team employed the Materials Project's database of materials energies, forces, and stresses to train the predictive M3GNet interatomic potential model. "We truly believe that the M3GNet architecture is a transformative tool that can greatly expand our ability to explore new material chemistries and structures," said Ong.

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An AI with Artificial Sleep

Novel thought,  We worked with UCSD.

AI Uses Artificial Sleep to Learn New Task without Forgetting the Last

By New Scientist, November 16, 2022

The researchers simulated sleep in the neural network by activating the networks artificial neurons in a noisy pattern. 

Experiments demonstrated the importance of having “rapidly alternating sessions of training and sleep” while the AI was learning a second task.

Scientists at the University of California, San Diego (UCSD) and the Czech Republic's Czech Academy of Sciences taught an artificial intelligence (AI) to learn a second distinct task without overwriting connections learned from a first task, through the use of simulated sleep.

UCSD's Erik Delanois said it was critical to "have rapidly alternating sessions of training and sleep" while the AI was learning the second task, which consolidated links from the first task that would have otherwise been forgotten ..... ' 

"Such a network will have the ability to combine consecutively learned knowledge in smart ways, and apply this learning to novel situations—just like animals and humans do," said the University of Massachusetts Amherst's Hava Siegelmann.

From New Scientist

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A Neuromorphic Chip for AI on the Edge

Chips for AI

 A Neuromorphic Chip for AI on the Edge

UC San Diego News Center

By Ioana Patringenaru, August 17, 2022

An international team of researchers created the NeuRRAM neuromorphic chip to compute directly in memory and run artificial intelligence (AI) applications with twice the energy efficiency of platforms for general-purpose AI computing. The chip moves AI closer to running on edge devices, untethered from the cloud; it also produces results as accurate as conventional digital chips, and supports many neural network models and architectures. "The conventional wisdom is that the higher efficiency of compute-in-memory is at the cost of versatility, but our NeuRRAM chip obtains efficiency while not sacrificing versatility," said former University of California, San Diego researcher Weier Wan. ... '

Tuberculosis Vaccine

 An example of pharma design

Computational Models Move Researchers Closer to Tuberculosis Vaccine

UC San Diego News Center,  Kimberly Mann Bruch, January 13, 2022

The University of Michigan's Denise Kirschner and colleagues are using supercomputers at the San Diego Supercomputer Center at the University of California, San Diego (UCSD) to investigate tuberculosis (TB), as a means of informing vaccine design. Kirschner said the work has shed light on the role of neutrophil cells in the immune response to TB infection. Predictive models using UCSD's Expanse supercomputer enabled the researchers to use images of neutrophils within TB granuloma to produce high-resolution models showing cells reacting to the disease. Said Kirschner, "Our computational approach and results from our recent study have helped narrow this concerning 'vaccine design space'—getting us closer to a globally effective vaccine."

Super Productive 3D Bioprinter Speeding Drug Development

Another example.  Have now heard many such claims regarding drug development. How many are being used effectively? Here is another. 

Super Productive 3D Bioprinter Could Speed Drug Development

UC San Diego Jacobs School of Engineering

June 8, 2021

A three-dimensional (3D) bioprinter developed by researchers at the University of California San Diego (UCSD) that can produce large batches of custom biological tissues at record speed could accelerate drug development. The new bioprinting method can produce a tissue sample in just 10 seconds, compared to hours with traditional methods. The researchers designed 3D models of biological structures on a computer, which slices the models into 2D snapshots and transfers them to millions of microscopic-sized mirrors, which are digitally controlled to project patterns of violet light in the form of these snapshots. After the light patterns are shined onto a solution that solidifies upon exposure to light, the structure is printed a layer at a time in a continuous fashion. UCSD’s Shangting You said, “What we are developing here are complex 3D cell culture systems that will more closely mimic actual human tissues, and that can hopefully improve the success rate of drug development.”  ... '

San Diego Supercomputing

 Long ago we worked with UCSD group, in the Bio Modeling area.

San Diego Supercomputer Center Helps Advance Computational Chemistry  By University of California San Diego, February 18, 2021

MIT's Heather Kulik and colleagues used the Comet supercomputer at the University of California, San Diego's San Diego Supercomputer Center and the Bridges supercomputer at the Pittsburgh Supercomputing Center in this effort. The resulting artificial neural network models predict strong correlation in materials at significantly lower computational cost than conventional models, potentially accelerating the search for materials in diverse applications.

The MIT team's  workflow engaged with at least three electronic structure codes and utilized central processing units and graphics processing units on Comet and Bridges. "Using those supercomputers firsthand allowed me to think about ways I can teach students who may just be learning computational chemistry to complement their experimental research for ways that they can use not only now but in the future," Kulik says. ...

From University of California San Diego