A new study by researchers at King’s Business School and Wazoku has revealed that AI is transforming global problem-solving. The report found that nearly half (46%) of Wazoku’s 700,000-strong network of problem solvers had utilised generative AI (GenAI) to work on innovative ideas over the past…
Cancer biologists discover a new mechanism for an old drug
Since the 1950s, a chemotherapy drug known as 5-fluorouracil has been used to treat many types of cancer, including blood cancers and cancers of the digestive tract.
Doctors have long believed that this drug works by damaging the building blocks of DNA. However, a new study from MIT has found that in cancers of the colon and other gastrointestinal cancers, it actually kills cells by interfering with RNA synthesis.
The findings could have a significant effect on how doctors treat many cancer patients. Usually, 5-fluorouracil is given in combination with chemotherapy drugs that damage DNA, but the new study found that for colon cancer, this combination does not achieve the synergistic effects that were hoped for. Instead, combining 5-FU with drugs that affect RNA synthesis could make it more effective in patients with GI cancers, the researchers say.
“Our work is the most definitive study to date showing that RNA incorporation of the drug, leading to an RNA damage response, is responsible for how the drug works in GI cancers,” says Michael Yaffe, a David H. Koch Professor of Science at MIT, the director of the MIT Center for Precision Cancer Medicine, and a member of MIT’s Koch Institute for Integrative Cancer Research. “Textbooks implicate the DNA effects of the drug as the mechanism in all cancer types, but our data shows that RNA damage is what’s really important for the types of tumors, like GI cancers, where the drug is used clinically.”
Yaffe, the senior author of the new study, hopes to plan clinical trials of 5-fluorouracil with drugs that would enhance its RNA-damaging effects and kill cancer cells more effectively.
Jung-Kuei Chen, a Koch Institute research scientist, and Karl Merrick, a former MIT postdoc, are the lead authors of the paper, which appears today in Cell Reports Medicine.
An unexpected mechanism
Clinicians use 5-fluorouracil (5-FU) as a first-line drug for colon, rectal, and pancreatic cancers. It’s usually given in combination with oxaliplatin or irinotecan, which damage DNA in cancer cells. The combination was thought to be effective because 5-FU can disrupt the synthesis of DNA nucleotides. Without those building blocks, cells with damaged DNA wouldn’t be able to efficiently repair the damage and would undergo cell death.
Yaffe’s lab, which studies cell signaling pathways, wanted to further explore the underlying mechanisms of how these drug combinations preferentially kill cancer cells.
The researchers began by testing 5-FU in combination with oxaliplatin or irinotecan in colon cancer cells grown in the lab. To their surprise, they found that not only were the drugs not synergistic, in many cases they were less effective at killing cancer cells than what one would expect by simply adding together the effects of 5-FU or the DNA-damaging drug given alone.
“One would have expected that these combinations to cause synergistic cancer cell death because you are targeting two different aspects of a shared process: breaking DNA, and making nucleotides,” Yaffe says. “Karl looked at a dozen colon cancer cell lines, and not only were the drugs not synergistic, in most cases they were antagonistic. One drug seemed to be undoing what the other drug was doing.”
Yaffe’s lab then teamed up with Adam Palmer, an assistant professor of pharmacology at the University of North Carolina School of Medicine, who specializes in analyzing data from clinical trials. Palmer’s research group examined data from colon cancer patients who had been on one or more of these drugs and showed that the drugs did not show synergistic effects on survival in most patients.
“This confirmed that when you give these combinations to people, it’s not generally true that the drugs are actually working together in a beneficial way within an individual patient,” Yaffe says. “Instead, it appears that one drug in the combination works well for some patients while another drug in the combination works well in other patients. We just cannot yet predict which drug by itself is best for which patient, so everyone gets the combination.”
These results led the researchers to wonder just how 5-FU was working, if not by disrupting DNA repair. Studies in yeast and mammalian cells had shown that the drug also gets incorporated into RNA nucleotides, but there has been dispute over how much this RNA damage contributes to the drug’s toxic effects on cancer cells.
Inside cells, 5-FU is broken down into two different metabolites. One of these gets incorporated into DNA nucleotides, and other into RNA nucleotides. In studies of colon cancer cells, the researchers found that the metabolite that interferes with RNA was much more effective at killing colon cancer cells than the one that disrupts DNA.
That RNA damage appears to primarily affect ribosomal RNA, a molecule that forms part of the ribosome — a cell organelle responsible for assembling new proteins. If cells can’t form new ribosomes, they can’t produce enough proteins to function. Additionally, the lack of undamaged ribosomal RNA causes cells to destroy a large set of proteins that normally bind up the RNA to make new functional ribosomes.
The researchers are now exploring how this ribosomal RNA damage leads cells to under programmed cell death, or apoptosis. They hypothesize that sensing of the damaged RNAs within cell structures called lysosomes somehow triggers an apoptotic signal.
“My lab is very interested in trying to understand the signaling events during disruption of ribosome biogenesis, particularly in GI cancers and even some ovarian cancers, that cause the cells to die. Somehow, they must be monitoring the quality control of new ribosome synthesis, which somehow is connected to the death pathway machinery,” Yaffe says.
New combinations
The findings suggest that drugs that stimulate ribosome production could work together with 5-FU to make a highly synergistic combination. In their study, the researchers showed that a molecule that inhibits KDM2A, a suppressor of ribosome production, helped to boost the rate of cell death in colon cancer cells treated with 5-FU.
The findings also suggest a possible explanation for why combining 5-FU with a DNA-damaging drug often makes both drugs less effective. Some DNA damaging drugs send a signal to the cell to stop making new ribosomes, which would negate 5-FU’s effect on RNA. A better approach may be to give each drug a few days apart, which would give patients the potential benefits of each drug, without having them cancel each other out.
“Importantly, our data doesn’t say that these combination therapies are wrong. We know they’re effective clinically. It just says that if you adjust how you give these drugs, you could potentially make those therapies even better, with relatively minor changes in the timing of when the drugs are given,” Yaffe says.
He is now hoping to work with collaborators at other institutions to run a phase 2 or 3 clinical trial in which patients receive the drugs on an altered schedule.
“A trial is clearly needed to look for efficacy, but it should be straightforward to initiate because these are already clinically accepted drugs that form the standard of care for GI cancers. All we’re doing is changing the timing with which we give them,” he says.
The researchers also hope that their work could lead to the identification of biomarkers that predict which patients’ tumors will be more susceptible to drug combinations that include 5-FU. One such biomarker could be RNA polymerase I, which is active when cells are producing a lot of ribosomal RNA.
The research was funded by the Damon Runyon Cancer Research Fund, a Ludwig Center at MIT Fellowship, the National Institutes of Health, the Ovarian Cancer Research Fund, the Holloway Foundation, and the STARR Cancer Consortium.
Extracting Training Data From Fine-Tuned Stable Diffusion Models
New research from the US presents a method to extract significant portions of training data from fine-tuned models. This could potentially provide legal evidence in cases where an artist’s style has been copied, or where copyrighted images have been used to train generative models of public…
Victor Ambros ’75, PhD ’79 and Gary Ruvkun share Nobel Prize in Physiology or Medicine
MIT alumnus Victor Ambros ’75, PhD ’79 and Gary Ruvkun, who did his postdoctoral training at MIT, will share the 2024 Nobel Prize in Physiology or Medicine, the Royal Swedish Academy of Sciences announced this morning in Stockholm.
Ambros, a professor at the University of Massachusetts Chan Medical School, and Ruvkun, a professor at Harvard Medical School and Massachusetts General Hospital, were honored for their discovery of microRNA, a class of tiny RNA molecules that play a critical role in gene control.
“Their groundbreaking discovery revealed a completely new principle of gene regulation that turned out to be essential for multicellular organisms, including humans. It is now known that the human genome codes for over one thousand microRNAs. Their surprising discovery revealed an entirely new dimension to gene regulation. MicroRNAs are proving to be fundamentally important for how organisms develop and function,” the Nobel committee said in its announcement today.
During the late 1980s, Ambros and Ruvkun both worked as postdocs in the laboratory of H. Robert Horvitz, a David H. Koch Professor at MIT, who was awarded the Nobel Prize in 2002.
While in Horvitz’s lab, the pair began studying gene control in the roundworm C. elegans — an effort that laid the groundwork for their Nobel discoveries. They studied two mutant forms of the worm, known as lin-4 and lin-14, that showed defects in the timing of the activation of genetic programs that control development.
In the early 1990s, while Ambros was a faculty member at Harvard University, he made a surprising discovery. The lin-4 gene, instead of encoding a protein, produced a very short RNA molecule that appeared in inhibit the expression of lin-14.
At the same time, Ruvkun was continuing to study these C. elegans genes in his lab at MGH and Harvard. He showed that lin-4 did not inhibit lin-14 by preventing the lin-14 gene from being transcribed into messenger RNA; instead, it appeared to turn off the gene’s expression later on, by preventing production of the protein encoded by lin-14.
The two compared results and realized that the sequence of lin-4 was complementary to some short sequences of lin-14. Lin-4, they showed, was binding to messenger RNA encoding lin-14 and blocking it from being translated into protein — a mechanism for gene control that had never been seen before. Those results were published in two articles in the journal Cell in 1993.
In an interview with the Journal of Cell Biology, Ambros credited the contributions of his collaborators, including his wife, Rosalind “Candy” Lee ’76, and postdoc Rhonda Feinbaum, who both worked in his lab, cloned and characterized the lin-4 microRNA, and were co-authors on one of the 1993 Cell papers.
In 2000, Ruvkun published the discovery of another microRNA molecule, encoded by a gene called let-7, which is found throughout the animal kingdom. Since then, more than 1,000 microRNA genes have been found in humans.
“Ambros and Ruvkun’s seminal discovery in the small worm C. elegans was unexpected, and revealed a new dimension to gene regulation, essential for all complex life forms,” the Nobel citation declared.
Ambros, who was born in New Hampshire and grew up in Vermont, earned his PhD at MIT under the supervision of David Baltimore, then an MIT professor of biology, who received a Nobel Prize in 1973. Ambros was a longtime faculty member at Dartmouth College before joining the faculty at the University of Massachusetts Chan Medical School in 2008.
Ruvkun is a graduate of the University of California at Berkeley and earned his PhD at Harvard University before joining Horvitz’s lab at MIT.
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On technology in schools, think evolution, not revolution
Back in 1913 Thomas Edison confidently proclaimed, “Books will soon be obsolete in the public schools.” At the time, Edison was advocating for motion pictures as an educational device. “Our school system will be completely changed inside of 10 years,” he added.
Edison was not wrong that video recordings could help people learn. On the other hand, students still read books today. Like others before and after him, Edison thought one particular technology was going to completely revolutionize education. In fact, technologies do get adopted into schools, but usually quite gradually and without altering the fundamentals of education: a good classroom with good teachers and a community of willing students.
The idea that technology changes education incrementally is central to Justin Reich’s work. Reich is an associate professor in MIT’s Comparative Media Studies/Writing program who has been studying schools for a couple of decades, as a teacher, consultant, and scholar. Reich is an advocate for technology, but with a realistic perspective.
Time after time, entrepreneurs claim tech will upend what they depict as stagnation in schools. Both parts of those claims usually miss the mark: Tech tools produce not revolution but evolution, in schools that are frequently changing anyway. Reich’s work emphasizes this alternate framework.
“In the history of education technology, the two most common findings are, first, when teachers get new technology, they use it to do what they were already doing,” Reich says. “It takes quite a bit of time, practice, coaching, messing up, trying again, and iteration, to have new technologies lead to new and better practices.”
The second finding, meanwhile, is that ed-tech tools are most readily adopted by the well-off.
“Almost every educational technology we’ve ever developed disproportionately benefits the affluent,” Reich says. “Even when we make things available for free, people with more financial, social, and technical capital are more likely to take advantage of innovations. Those are two findings from the research literature that people don’t want to hear.”
Some people must want to hear them: Reich has written two well-regarded books about education, and for his scholarship and teaching was awarded tenure earlier this year at MIT, where he founded the Teaching Systems Lab.
“I’ve spent a substantial portion of my career reminding people of those two things, and demonstrating them again and again,” Reich says.
Optimized like a shark
Long before he made a living by studying schools, Reich pictured himself working in them. Indeed, that was his career plan.
“I wanted to be a teacher,” Reich says. He received his undergraduate degree from the University of Virginia in interdisciplinary studies, then earned an MA in history from the University of Virginia, writing a thesis about the U.S. National Parks system.
Reich then got a job in the early 2000s as a history teacher at a private school in the Boston area. Soon the school administrators gave Reich a cart of laptops and encouraged him to put the new tools to use. Many history archives were becoming digitized, so Reich happily integrated the laptops and web-based sources into his lessons.
Before long Reich co-founded EdTechTeacher, a consulting firm helping schools use technology productively. And his own teaching reinforced a lesson: When larger practices in a discipline change, schools can use technology to follow suit; it will make less difference otherwise. Then too, schools also adapt and evolve in ways unrelated to technology. For instance, we now educate a greater breadth of people than ever.
“You can absolutely improve schools,” Reich says. “And we improve schools all the time. It’s just a long, slow process, and everything is kind of incremental.”
Eventually Reich went back to school himself, earning his PhD from Harvard University’s Graduate School of Education in 2012. At the time, large-scale online college courses were seen as a potentially disruptive force in higher education. But that proposed revolution became an evolution, with online learning producing uneven results for K-12 students and undergraduates while being used more effectively in some graduate programs. Reich examines the subject in his 2020 book, “Failure to Disrupt,” about technologies intended to enhance education at scale.
“Online learning is good for people who are already well-equipped for learning, and those tend to be well-off, educated people,” Reich says. The Covid-19 pandemic also helped reinforce the value of in-person learning. The physical classroom may date to ancient times, but it is a durable innovation.
“Technology gets introduced into educational systems, when it’s possible that the systems are already pretty optimized for what they want to do,” Reich says. Citing another scholar of education, he notes, “Mike Caufield says, ‘We think of schools as old and ancient, but maybe they are in the way a great white shark is, optimized for its environment.’”
Okay, but what about AI?
Reich has now seen many supposed ed-tech revolutions firsthand and studied many others from the past. The latest such potential revolution, of course, is artificial intelligence, currently subject of massive investments and attention. Will AI be different, and fundamentally transform the way we learn? Reich and a colleague, Jesse Dukes, are conducting a research project finding out how schools are currently using AI. So far, Reich thinks, the impact is not huge.
“A lot of folks are saying, ‘AI is going to be amazing! It’s going to transform everything!’” Reich says. “And we’re spending a lot of time with teachers and students asking what they’re actually doing. And of course AI is not transformative. Teachers are finding modest ways to integrate it into their practice, but the main function of AI in schools is kids using it to do their homework, which is probably not good for learning, on net.”
To some degree, Reich suspects, teachers are now devoting more time to in-class writing assignments, to work around students substituting Chat GPT for their own writing. As he notes, “Using in-class time differently to accommodate for changes in technology is something educators have gotten really good at doing over the last decade. This doesn’t seem like a tidal wave crashing over them.”
Reich, again, is not an opponent of technology, but a realist about it, including AI. “A lot of new things are probably helpful in some way, some place, so let’s find it,” he says. In the meantime, schools will be grappling with a lot of hard problems that tech alone will not solve.
“If you’re working at a school serving kids furthest from opportunity in the country, the biggest problem you’re facing right now is chronic absenteeism,” Reich says. “You’re having a really hard time getting kids to show up. AI doesn’t really have anything to do with that.”
Overall, Reich thinks, the key in sustaining good schools is to keep tinkering on many fronts. Educators should “act in short design spirals,” as he wrote in his 2023 book, “Iterate: The Secret to Innovation in Schools,” rather than waiting for radical technology solutions. In education, the tortoise will usually beat the disruptor.
“Improving education is a lot of hard work, and it’s a long process, but at the other end of it, you can get genuine improvement,” Reich concludes.
How MIT’s Clio Enhances Scene Understanding for Robotics
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Adobe Firefly Brings Generative AI to Video – Videoguys
In this article from Adobe, the company reflects on the rapid advancements made with Adobe Firefly, its innovative generative AI tool, since its initial launch in March 2023. Authored by Adobe, the article highlights the impressive integration of new Firefly models into widely-used tools such as Photoshop, Lightroom, Illustrator, and Express. With these tools, users can leverage Firefly-powered features like Generative Fill in Photoshop and Generative Remove in Lightroom, transforming the way creators and designers work. The creative community has embraced these features, generating over 12 billion images and vectors, making Adobe Firefly one of the fastest-adopted AI technologies within Creative Cloud and Express.
As Adobe expands its generative AI capabilities, the upcoming Firefly Video Model is set to revolutionize the video editing landscape. Available in Premiere Pro, starting with the beta version later this year, this model will enable editors to use AI tools to streamline their workflows. Adobe has worked closely with the video editing community to fine-tune the Firefly Video Model, ensuring it meets the needs of professionals. Like other Firefly models, the video AI tool is trained on content that Adobe has permission to use, ensuring a commercially safe environment for creators.
As the demand for short-form video content grows, Adobe is helping video editors, filmmakers, and content creators meet these challenges head-on. Editors are now tasked with color correction, visual effects, titling, animation, and audio mixing, in addition to traditional editing tasks. Adobe’s AI-driven tools, including Firefly Text-to-Video, help editors fill gaps in footage, remove unwanted objects, and generate B-roll using simple text prompts. These AI-powered solutions not only save time but also enable editors to focus on creative storytelling and collaboration, delivering high-quality work faster.
Adobe Firefly is changing the way editors and creators work, helping them overcome common challenges and elevate their creative output. By integrating generative AI into their workflows, Adobe is enabling professionals to create their best work in record time, setting new standards in the world of video editing.
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Read the full article by Ashley Still for Adobe HERE