Technique allows researchers to toggle on individual genes that regulate cell growth, development, and function.
By combining CRISPR technology with a protein designed with artificial intelligence, it is possible to awaken individual dormant genes by disabling the chemical “off switches” that silence them. Researchers from the University of Washington School of Medicine in Seattle describe this finding in the journal Cell Reports.
The approach will allow researchers to understand the role individual genes play in normal cell growth and development, in aging, and in such diseases as cancer, said Shiri Levy, a postdoctoral fellow in UW Institute for Stem Cell and Regenerative Medicine (ISCRM) and the lead author of the paper.
Anion exchange membranes (AEMs) are semipermeable fuel cell components that can conduct anions but reject cations and gases. This enables the partition of substances that could chemically react with one another, thus allowing the cells to operate properly.
A team of researchers at Tianjin University in China have recently developed new types of AEMs that are based on a newly designed ferrocenium material. Their membranes, presented in a paper published in Nature Energy, were found to achieve highly promising results in terms of power output, durability, and ohmic resistance.
“As the oriented mixed-valence ferrocenium material developed in our study is entirely new for the AEM field, we encountered many difficulties and frustrations along the way,” Michael D. Guiver, one of the researchers who carried out the study, told TechXplore. “We spent a long research period and much effort, both experimentally and theoretically, to achieve these good outcomes. The whole process from initial conceptualization to final publication was convoluted, but fortunately successful.”
Devastating residential blazes and wildfires take a terrible toll in terms of deaths and injuries, as well as property loss. Today, researchers will report on a new type of coating that could limit the flammability of wood used in construction, potentially providing more time to escape fires and also curbing their spread. The environmentally friendly flame retardant could also be used for other flammable materials, such as textiles, polyurethane foam and 3D-printed parts.
The researchers will present their results today at the spring meeting of the American Chemical Society (ACS).
Home fires account for the majority of fire deaths and lead to billions of dollars in property damage every year, according to the National Fire Protection Association. Adding fire sprinklers and smoke detectors can help, but another approach is to make construction materials less flammable. That’s the goal of Thomas Kolibaba, Ph.D., who is developing a new coating for these materials. “This type of treatment, which could be deposited via dipping, spraying or pressure treatment, could make homes much safer,” he says. “The coating could reduce flame spread and smoke production, which could limit damage and give people more time to evacuate.” Unlike most current fire retardant treatments, its ingredients are environmentally benign, and it might also cost less, notes Jaime Grunlan, Ph.D., the project’s principal investigator.
Biomedical Interventions For Substantial Global Health Concerns — Dr. Emilio Emini, Ph.D., CEO, Bill & Melinda Gates Medical Research Institute
Dr. Emilio A. Emini, Ph.D. is the CEO of the Bill & Melinda Gates Medical Research Institute (https://www.gatesmri.org/), a non-profit organization dedicated to the development and effective use of novel biomedical interventions addressing substantial global health concerns, for which investment incentives are limited, and he leads the Institute’s research and development of novel products and interventions for diseases disproportionately impacting the world’s most vulnerable populations.
Before joining the Gates MRI, Dr. Emini served as director of the HIV and Tuberculosis program at the Bill & Melinda Gates Foundation, where he led the foundation’s efforts focused on accelerating the reduction in the incidence of HIV and TB in high-burden geographies, with the goal of achieving sustained epidemic control.
Over the course of his previous 30-year career in the bio-pharmaceutical industry, Dr. Emini led teams involved in the research and development of novel anti-infectives and vaccines. From 1983 to 2004, he led research at the Merck Research Laboratories involved in the development of one of the first highly active anti-retroviral therapies for HIV and, as senior vice president of vaccine research, the successful development of a number of vaccines including vaccines for human papillomavirus and rotavirus.
Dr. Emini later served as senior vice president of vaccine development at the International AIDS Vaccine Initiative. From 2005 to 2015, he was senior vice president of vaccine R&D at Pfizer Inc., leading the development of Prevnar 13® for prevention of pneumococcal disease.
Years of toil in the laboratory have revealed how a marine bacterium makes a potent anti-cancer molecule.
The anti-cancer molecule salinosporamide A, also called Marizomb, is in Phase III clinical trials to treat glioblastoma, a brain cancer. Scientists now for the first time understand the enzyme-driven process that activates the molecule.
Researchers at UC San Diego’s Scripps Institution of Oceanography found that an enzyme called SalC assembles what the team calls the salinosporamide anti-cancer “warhead.” Scripps graduate student Katherine Bauman is the lead author of a paper that explains the assembly process in the March 21 issue of Nature Chemical Biology.
A.I. is only beginning to show what it can do for modern medicine.
In today’s society, artificial intelligence (A.I.) is mostly used for good. But what if it was not?
Naive thinking “The thought had never previously struck us. We were vaguely aware of security concerns around work with pathogens or toxic chemicals, but that did not relate to us; we primarily operate in a virtual setting. Our work is rooted in building machine learning models for therapeutic and toxic targets to better assist in the design of new molecules for drug discovery,” wrote the researchers in their paper. “We have spent decades using computers and A.I. to improve human health—not to degrade it. We were naive in thinking about the potential misuse of our trade, as our aim had always been to avoid molecular features that could interfere with the many different classes of proteins essential to human life.”
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Researchers from Collaborations Pharmaceuticals tweaked artificial intelligence to look for chemical weapons, and impressively enough the machine learning algorithm found 40,000 options in just six hours.
The researchers simulated the molecules H4, molecular nitrogen, and solid diamond. These involved as many as 120 orbitals, the patterns of electron density formed in atoms or molecules by one or more electrons. These are the largest chemistry simulations performed to date with the help of quantum computers.
A classical computer actually handles most of this fermionic quantum Monte Carlo simulation. The quantum computer steps in during the last, most computationally complex step—calculating the differences between the estimates of the ground state made by the quantum computer and the classical computer.
The prior record for chemical simulations with quantum computing employed 12 qubits and a kind of hybrid algorithm known as a variational quantum eigensolver (VQE). However, VQEs possess a number of limitations compared with this new hybrid approach. For example, when one wants a very precise answer from a VQE, even a small amount of noise in the quantum circuitry “can cause enough of an error in our estimate of the energy or other properties that’s too large,” says study coauthor William Huggins, a quantum physicist at Google Quantum AI in Mountain View, Calif.
Janice Chen, Ph.D., one of Olympic gold medalist Nathan Chen’s siblings, is on a mission to build a $100 billion biotech company.
In 2018, she co-founded Mammoth Biosciences with Trevor Martin, Lucas Harrington and Jennifer Doudna 0, who won the Nobel Prize in Chemistry two years later for her pioneering work in CRISPR gene editing. Doudna also served as Chen’s mentor while she pursued her doctorate degree in molecular and cell biology at the University of California at Berkeley.
Mammoth is built on Chen’s work as a graduate student researcher in Doudna’s lab. Since the dawn of COVID-19 in 2020, the startup has seen accelerated growth as it snagged $100 million in multiple contracts and government grants.
Quantum computers are getting bigger, but there are still few practical ways to take advantage of their extra computing power. To get over this hurdle, researchers are designing algorithms to ease the transition from classical to quantum computers. In a new study in Nature, researchers unveil an algorithm that reduces the statistical errors, or noise, produced by quantum bits, or qubits, in crunching chemistry equations.
Developed by Columbia chemistry professor David Reichman and postdoc Joonho Lee with researchers at Google Quantum AI, the algorithm uses up to 16 qubits on Sycamore, Google’s 53-qubitcomputer, to calculate ground state energy, the lowest energy state of a molecule. “These are the largest quantum chemistry calculations that have ever been done on a real quantum device,” Reichman said.
The ability to accurately calculate ground state energy, will enable chemists to develop new materials, said Lee, who is also a visiting researcher at Google Quantum AI. The algorithm could be used to design materials to speed up nitrogen fixation for farming and hydrolysis for making clean energy, among other sustainability goals, he said.
Artificial intelligence advances how scientists explore materials. Researchers from Ames Laboratory and Texas A&M University trained a machine-learning (ML) model to assess the stability of rare-earth compounds. This work was supported by Laboratory Directed Research and Development Program (LDRD) program at Ames Laboratory. The framework they developed builds on current state-of-the-art methods for experimenting with compounds and understanding chemical instabilities.
Ames Lab has been a leader in rare-earths research since the middle of the 20th century. Rare earth elements have a wide range of uses including clean energy technologies, energy storage, and permanent magnets. Discovery of new rare-earth compounds is part of a larger effort by scientists to expand access to these materials.
The present approach is based on machine learning (ML), a form of artificial intelligence (AI), which is driven by computer algorithms that improve through data usage and experience. Researchers used the upgraded Ames Laboratory Rare Earth database (RIC 2.0) and high-throughput density-functional theory (DFT) to build the foundation for their ML model.
