It’s possible that I shall make an ass of myself. But in that case one can always get out of it with a little dialectic. I have, of course, so worded my proposition as to be right either way (K.Marx, Letter to F.Engels on the Indian Mutiny)
Friday, May 05, 2023
Married couples who merge finances may be happier, stay together longer
BLOOMINGTON, Ind. — The Beatles famously sang, “Money can't buy me love,” but married couples who manage their finances together may love each other longer, according to research from the Indiana University Kelley School of Business.
Prior research suggests a correlation that couples who merge finances tend to be happier than those who do not. But this is the first research to show a causal relationship — that married couples who have joint bank accounts not only have better relationships, but they fight less over money and feel better about how household finances are handled.
“When we surveyed people of varying relationship lengths, those who had merged accounts reported higher levels of communality within their marriage compared to people with separate accounts, or even those who partially merged their finances,” said Jenny Olson, assistant professor of marketing at Kelley. “They frequently told us they felt more like they were ‘in this together.’
“This is the best evidence that we have to date for a question that shapes couples’ futures; and the fact that we observe these meaningful shifts over two years, I think it's a pretty powerful testament to the benefits of merging. On average, merging should warrant a conversation with your partner, given the effects that we're seeing here.”
Olson and her co-authors recruited 230 couples, who were either engaged or newly married at the time, and followed them over two years as they began their married lives together. Everyone began the study with separate accounts and consented to potentially changing their financial arrangements. This was the first marriage for everyone involved in the study.
Some couples were then randomly assigned to keep their separate bank accounts, and others were told to open a joint bank account instead. A third group was allowed to make the decision on their own.
Couples who were told to open joint bank accounts reported substantially higher relationship quality two years later than those who maintained separate accounts, Olson said, adding that merging promotes greater financial goal alignment and transparency, and a communal understanding of marriage.
“A communal relationship is one where partners respond to each other’s needs because there’s a need. ‘I want to help you because you need it. I’m not keeping track,’” she said. “There’s a ‘we’ perspective, which we theorized would be related to a joint bank account.”
Olson said that couples with separate accounts viewed financial decision-making as more of an exchange.
“It’s ‘I help you because you’re going to help me later,’” she said. “They’re prepaying for later favors, and that’s tit-for-tat, which we see a bit more with separate accounts. It’s ‘I’ve got the Netflix bill and you pay the doctor.’ … They’re not working together like those with joint accounts — who have the same pool of money — and that’s more common in business-type relationships.”
With separate accounts, those in a marriage potentially may think it is easier to leave the relationship, Olson said. Twenty percent of participating couples did not finish the study, including a significant percentage of those who separated after not merging bank accounts. They found no gender differences in the results.
The mean age of participants was 28 years old. Three quarters were white, and 12 percent were Black. Thirty-six percent had a bachelor’s degree and a median household income of $50,000. Couples had known each other, on average, about five years and had been romantically involved for an average of three years. Ten percent had children.
Other study authors are Scott I. Rick, associate professor of marketing at the Ross School of Business at the University of Michigan; Deborah A. Small, the Adrian C. Israel Professor of Marketing at the Yale School of Management; and Eli J. Finkel, professor of management and organizations at the Kellogg School of Management and a professor of psychology at Northwestern.
Professor Olson recruited subjects for her research while attending bridal showers before her wedding.
Common Cents: Bank Account Structure and Couples’ Relationship Dynamics
SMART researchers create world’s smallest LED and holographic microscope that enable conversion of existing mobile phone cameras into high-resolution microscopes
This is the world’s smallest silicon (Si) light-emitting diode (LED) - smaller than the wavelength of light - with a light intensity comparable to much larger, state-of-the-art Si LEDs and with multiple potential applications
SINGAPORE-MIT ALLIANCE FOR RESEARCH AND TECHNOLOGY (SMART)
IMAGE: (A) PHOTOGRAPH OF A FULLY FABRICATED 300 MM WAFER. (B) CLOSE-UP OF A CHIP DIE. (C) INFRARED MICROGRAPH WITH THE LED TURNED ON. (D) HOLOGRAPHIC MICROSCOPE SETUP. (E) CLOSE-UP OF A RECONSTRUCTED HOLOGRAPHIC IMAGE COMPARED WITH THE (F) GROUND TRUTH.view more
CREDIT: PHOTO CREDIT: SINGAPORE-MIT ALLIANCE FOR RESEARCH AND TECHNOLOGY (SMART)
This is the world’s smallest silicon (Si) light-emitting diode (LED) - smaller than the wavelength of light - with a light intensity comparable to much larger, state-of-the-art Si LEDs and with multiple potential applications
This LED was used to build the world’s smallest holographic microscope and is a proof of concept that enables the cameras in existing devices (such as mobile phones) to be converted into high-resolution microscopes by only modifying the silicon chip and software
Complementing this is a new neural networking algorithm developed by SMART that is able to reconstruct objects measured by this microscope, including plant seeds and tissue samples - allowing for enhanced microscopic examination of a wide range of objects that was not previously possible as well as the detection of plant disease and aberrant plant tissue
Singapore, 4 May 2023 - Researchers from the Disruptive & Sustainable Technologies for Agricultural Precision (DiSTAP) and the Critical Analytics for Manufacturing Personalized-Medicine (CAMP) Interdisciplinary Research Groups (IRG) of Singapore-MIT Alliance for Research and Technology (SMART), MIT’s research enterprise in Singapore have developed the world’s smallest LED (light emitting diode) that enables the conversion of existing mobile phone cameras into high-resolution microscopes. Smaller than the wavelength of light, the new LED was used to build the world’s smallest holographic microscope, paving the way for existing cameras in everyday devices such as mobile phones to be converted into microscopes via only modifications to the silicon chip and software. This technology also represents a significant step forward in the miniaturisation of diagnostics for indoor farmers and sustainable agriculture.
This breakthrough was supplemented by the researchers’ development of a revolutionary neural networking algorithm that is able to reconstruct objects measured by the holographic microscope, thus enabling enhanced examination of microscopic objects such as cells and bacteria without the need for bulky conventional microscopes or additional optics. The research also paves the way for a major advancement in photonics - the building of a powerful on-chip emitter that is smaller than a micrometre, which has long been a challenge in the field.
The light in most photonic chips originates from off-chip sources, which leads to low overall energy efficiency and fundamentally limits the scalability of these chips. To address this issue, researchers have developed on-chip emitters using various materials such as rare-earth-doped glass, Ge-on-Si, and heterogeneously integrated III–V materials. While emitters based on these materials have shown promising device performance, integrating their fabrication processes into standard complementary metal-oxide-semiconductor (CMOS) platforms remains challenging. While silicon (Si) has shown potential as a candidate material for nanoscale and individually controllable emitters, Si emitters suffer from low quantum efficiency because of the indirect bandgap, and this fundamental disadvantage combined with the limitations set by the available materials and fabrication tools has hindered the realisation of a small native Si emitter in CMOS.
In a recently published Nature Communications paper titled, “A sub-wavelength Si LED integrated in a CMOS platform”, SMART researchers described their development of the smallest reported Si emitter with a light intensity comparable to that of state-of-the-art Si emitters with much larger emission areas. In a related breakthrough, SMART researchers also unveiled their construction of a novel, untrained deep neural network architecture capable of reconstructing images from a holographic microscope in a paper titled, “Simultaneous spectral recovery and CMOS micro-LED holography with an untrained deep neural network” recently published in the journal Optica.
The novel LED developed by SMART researchers is a CMOS-integrated sub-wavelength scale LED at room temperature exhibiting high spatial intensity (102 ± 48 mW/cm2) and possessing the smallest emission area (0.09 ± 0.04 μm2) among all known Si emitters in scientific literature. In order to demonstrate a potential practical application, the researchers then integrated this LED into an in-line, centimetre-scale, all-silicon holographic microscope requiring no lens or pinhole, integral to a field known as lensless holography.
A commonly faced obstacle in lensless holography is computational reconstruction of the imaged object. Traditional reconstruction methods require detailed knowledge of the experimental setup for accurate reconstruction and are sensitive to difficult-to-control variables such as optical aberrations, the presence of noise, and the twin image problem.
The research team also developed a deep neural network architecture to improve the quality of image reconstruction. This novel, untrained deep neural network incorporates total variation regularisation for increased contrast and takes into account the wide spectral bandwidth of the source. Unlike traditional methods of computational reconstruction that require training data, this neural network eliminates the need for training by embedding a physics model within the algorithm. In addition to holographic image reconstruction, the neutral network also offers blind source spectrum recovery from a single diffracted intensity pattern, which marks a groundbreaking departure from all previous supervised learning techniques.
The untrained neural network demonstrated in this study allows researchers to use novel light sources without prior knowledge of the source spectrum or beam profile, such as the novel and smallest known Si LED described above, fabricated via a fully commercial, unmodified bulk CMOS microelectronics.
The researchers envision that this synergetic combination of CMOS micro-LEDs and the neural network can be used in other computational imaging applications, such as a compact microscope for live-cell tracking or spectroscopic imaging of biological tissues such as living plants. This work also demonstrates the feasibility of next-generation on-chip imaging systems. Already, in-line holography microscopes have been employed for a variety of applications, including particle tracking, environmental monitoring, biological sample imaging, and metrology. Further applications include arraying these LEDs in CMOS to generate programmable coherent illumination for more complex systems in the future.
Iksung Kang, lead author of the Optica paper and Research Assistant at MIT at the time of this research, said, “Our breakthrough represents a proof of concept that could be hugely impactful for numerous applications requiring the use of micro-LEDs. For instance, this LED could be combined into an array for higher levels of illumination needed for larger-scale applications. In addition, due to the low cost and scalability of microelectronics CMOS processes, this can be done without increasing the system’s complexity, cost, or form factor. This enables us to convert, with relative ease, a mobile phone camera into a holographic microscope of this type. Furthermore, control electronics and even the imager could be integrated into the same chip by exploiting the available electronics in the process, thus creating an ‘all-in-one’ micro-LED that could be transformative for the field.”
“On top of its immense potential in lensless holography, our new LED has a wide range of other possible applications. Because its wavelength is within the minimum absorption window of biological tissues, together with its high intensity and nanoscale emission area, our LED could be ideal for bio-imaging and bio-sensing applications, including near-field microscopy and implantable CMOS devices,” added Rajeev Ram, Principal Investigator at SMART CAMP and DiSTAP, Professor of Electrical Engineering at MIT and co-author of both papers. “Also, it is possible to integrate this LED with on-chip photodetectors, and it could then find further applications in on-chip communication, NIR proximity sensing, and on-wafer testing of photonics.”
This research was carried out by SMART and supported by the National Research Foundation (NRF) Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme.
IMAGE: PROFESSOR PHILIP KIM AND JIN SUB (MICHAEL) LEEview more
CREDIT: UNIVERSITY OF TORONTO
Researchers at the University of Toronto have developed an artificial intelligence system that can create proteins not found in nature using generative diffusion, the same technology behind popular image-creation platforms such as DALL-E and Midjourney.
The system will help advance the field of generative biology, which promises to speed drug development by making the design and testing of entirely new therapeutic proteins more efficient and flexible.
“Our model learns from image representations to generate fully new proteins, at a very high rate,” says Philip M. Kim, a professor in the Donnelly Centre for Cellular and Biomolecular Research at U of T’s Temerty Faculty of Medicine. “All our proteins appear to be biophysically real, meaning they fold into configurations that enable them to carry out specific functions within cells.”
Today, the journal Nature Computational Sciencepublished the findings, the first of their kind in a peer-reviewed journal. Kim’s lab also published a pre-print on the model last summer through the open-access server bioRxiv, ahead of two similar pre-prints from last December, RF Diffusion by the University of Washington and Chroma by Generate Biomedicines.
Proteins are made from chains of amino acids that fold into three-dimensional shapes, which in turn dictate protein function. Those shapes evolved over billions of years and are varied and complex, but also limited in number. With a better understanding of how existing proteins fold, researchers have begun to design folding patterns not produced in nature.
But a major challenge, says Kim, has been to imagine folds that are both possible and functional. “It’s been very hard to predict which folds will be real and work in a protein structure,” says Kim, who is also a professor in the departments of molecular genetics and computer science at U of T. “By combining biophysics-based representations of protein structure with diffusion methods from the image generation space, we can begin to address this problem.”
The new system, which the researchers call ProteinSGM, draws from a large set of image-like representations of existing proteins that encode their structure accurately. The researchers feed these images into a generative diffusion model, which gradually adds noise until each image becomes all noise. The model tracks how the images become noisier and then runs the process in reverse, learning how to transform random pixels into clear images that correspond to fully novel proteins.
Jin Sub (Michael) Lee, a doctoral student in the Kim lab and first author on the paper, says that optimizing the early stage of this image generation process was one of the biggest challenges in creating ProteinSGM. “A key idea was the proper image-like representation of protein structure, such that the diffusion model can learn how to generate novel proteins accurately,” says Lee, who is from Vancouver but did his undergraduate degree in South Korea and master’s in Switzerland before choosing U of T for his doctorate.
Also difficult was validation of the proteins produced by ProteinSGM. The system generates many structures, often unlike anything found in nature. Almost all of them look real according to standard metrics, says Lee, but the researchers needed further proof.
To test their new proteins, Lee and his colleagues first turned to OmegaFold, an improved version of DeepMind’s software AlphaFold 2. Both platforms use AI to predict the structure of proteins based on amino acid sequences.
With OmegaFold, the team confirmed that almost all their novel sequences fold into the desired and also novel protein structures. They then chose a smaller number to create physically in test tubes, to confirm the structures were proteins and not just stray strings of chemical compounds.
“With matches in OmegaFold and experimental testing in the lab, we could be confident these were properly folded proteins. It was amazing to see validation of these fully new protein folds that don’t exist anywhere in nature,” Lee says.
Next steps based on this work include further development of ProteinSGM for antibodies and other proteins with the most therapeutic potential, Kim says. “This will be a very exciting area for research and entrepreneurship,” he adds.
Lee says he would like to see generative biology move toward joint design of protein sequences and structures, including protein side-chain conformations. Most research to date has focussed on generation of backbones, the primary chemical structures that hold proteins together.
“Side-chain configurations ultimately determine protein function, and although designing them means an exponential increase in complexity, it may be possible with proper engineering,” Lee says. “We hope to find out.”
Score-based generative modeling for de novo protein design
ARTICLE PUBLICATION DATE
4-May-2023
UMD leads new $20M NSF Institute for Trustworthy AI in Law and Society
The institute will take a holistic approach, integrating broader participation in artificial intelligence design, new technology development, and more informed governance of AI-infused systems
IMAGE: THE NSF INSTITUTE FOR TRUSTWORTHY AI IN LAW & SOCIETY (TRAILS), LED BY THE UNIVERSITY OF MARYLAND, WAS ANNOUNCED ON MAY 4, 2023.view more
CREDIT: NSF INSTITUTE FOR TRUSTWORTHY AI IN LAW & SOCIETY
The University of Maryland has been chosen to lead a multi-institutional effort supported by the National Science Foundation (NSF) that will develop new artificial intelligence (AI) technologies designed to promote trust and mitigate risks, while simultaneously empowering and educating the public.
The NSF Institute for Trustworthy AI in Law & Society (TRAILS) announced on May 4, 2023, unites specialists in AI and machine learning with social scientists, legal scholars, educators and public policy experts. The multidisciplinary team will work with impacted communities, private industry and the federal government to determine what trust in AI looks like, how to develop technical solutions for AI that can be trusted, and which policy models best create and sustain trust.
Funded by a $20 million award from NSF, the new institute is expected to transform the practice of AI from one driven primarily by technological innovation to one that is driven by ethics, human rights, and input and feedback from communities whose voices have previously been marginalized.
“As artificial intelligence continues to grow exponentially, we must embrace its potential for helping to solve the grand challenges of our time, as well as ensure that it is used both ethically and responsibly,” said UMD President Darryll J. Pines. “With strong federal support, this new institute will lead in defining the science and innovation needed to harness the power of AI for the benefit of the public good and all humankind.”
In addition to UMD, TRAILS will include faculty members from George Washington University (GW) and Morgan State University, with more support coming from Cornell University, the National Institute of Standards and Technology (NIST), and private sector organizations like the DataedX Group, Arthur AI, Checkstep, FinRegLab and Techstars.
At the heart of establishing the new institute is the consensus that AI is currently at a crossroads. AI-infused systems have great potential to enhance human capacity, increase productivity, catalyze innovation, and mitigate complex problems, but today’s systems are developed and deployed in a process that is opaque and insular to the public, and therefore, often untrustworthy to those affected by the technology.
“We’ve structured our research goals to educate, learn from, recruit, retain and support communities whose voices are often not recognized in mainstream AI development,” said Hal Daumé III, a UMD professor of computer science who is lead principal investigator of the NSF award and will serve as the director of TRAILS.
Inappropriate trust in AI can result in many negative outcomes, Daumé said. People often “overtrust” AI systems to do things they’re fundamentally incapable of. This can lead to people or organizations giving up their own power to systems that are not acting in their best interest. At the same time, people can also “undertrust” AI systems, leading them to avoid using systems that could ultimately help them.
Given these conditions—and the fact that AI is increasingly being deployed to mediate society’s online communications, determine health care options, and offer guidelines in the criminal justice system—it has become urgent to ensure that people’s trust in AI systems matches those same systems’ level of trustworthiness.
(From left) University of Maryland doctoral student Lovely-Frances Domingo, Professor Hal Daumé III and Associate Professor Katie Shilton discuss some of Shilton’s work on ethics and policy for the design of information technologies. Daumé and Shilton are helping lead the new $20M NSF Institute for Trustworthy AI in Law & Society.
CREDIT
Maria Herd
TRAILS has identified four key research thrusts to promote the development of AI systems that can earn the public’s trust through broader participation in the AI ecosystem.
The first, known as participatory AI, advocates involving human stakeholders in the development, deployment and use of these systems. It aims to create technology in a way that aligns with the values and interests of diverse groups of people, rather than being controlled by a few experts or solely driven by profit.
Leading the efforts in participatory AI is Katie Shilton, an associate professor in UMD’s College of Information Studies who specializes in ethics and sociotechnical systems. Tom Goldstein, a UMD associate professor of computer science, will lead the institute’s second research thrust, developing advanced machine learning algorithms that reflect the values and interests of the relevant stakeholders.
Daumé, Shilton and Goldstein all have appointments in the University of Maryland Institute for Advanced Computer Studies, which is providing administrative and technical support for TRAILS.
David Broniatowski, an associate professor of engineering management and systems engineering at GW, will lead the institute’s third research thrust of evaluating how people make sense of the AI systems that are developed, and the degree to which their levels of reliability, fairness, transparency and accountability will lead to appropriate levels of trust. Susan Ariel Aaronson, a research professor of international affairs at GW, will use her expertise in data-driven change and international data governance to lead the institute’s fourth thrust of participatory governance and trust.
Virginia Byrne, an assistant professor of higher education and student affairs at Morgan State, will lead community-driven projects related to the interplay between AI and education. According to Daumé, the TRAILS team will rely heavily on Morgan State’s leadership—as Maryland’s preeminent public urban research university—in conducting rigorous, participatory community-based research with broad societal impacts.
Additional academic support will come from Valerie Reyna, a professor of human development at Cornell, who will use her expertise in human judgment and cognition to advance efforts focused on how people interpret their use of AI.
Federal officials at NIST will collaborate with TRAILS in the development of meaningful measures, benchmarks, test beds and certification methods—particularly as they apply to important topics essential to trust and trustworthiness such as safety, fairness, privacy, transparency, explainability, accountability, accuracy and reliability.
“The ability to measure AI system trustworthiness and its impacts on individuals, communities and society is limited. TRAILS can help advance our understanding of the foundations of trustworthy AI, ethical and societal considerations of AI, and how to build systems that are trusted by the people who use and are affected by them,” said Under Secretary of Commerce for Standards and Technology and NIST Director Laurie E. Locascio.
Today’s announcement is the latest in a series of federal grants establishing a cohort of National Artificial Intelligence Research Institutes. This recent investment in seven new AI institutes, totaling $140 million, follows two previous rounds of awards.
“Maryland is at the forefront of our nation’s scientific innovation thanks to our talented workforce, top-tier universities, and federal partners,” said U.S. Sen. Chris Van Hollen (D-Md.). “This National Science Foundation award for the University of Maryland—in coordination with other Maryland-based research institutions including Morgan State University and NIST—will promote ethical and responsible AI development, with the goal of helping us harness the benefits of this powerful emerging technology while limiting the potential risks it poses. This investment entrusts Maryland with a critical priority for our shared future, recognizing the unparalleled ingenuity and world-class reputation of our institutions.”
The NSF, in collaboration with government agencies and private sector leaders, has now invested close to half a billion dollars in the AI institutes ecosystem—an investment that expands a collaborative AI research network into almost every U.S. state.
“The National AI Research Institutes are a critical component of our nation’s AI innovation, infrastructure, technology, education and partnerships ecosystem,” said NSF Director Sethuraman Panchanathan. “[They] are driving discoveries that will ensure our country is at the forefront of the global AI revolution.”
AI could run a million microbial experiments per year
Automation uncovers combinations of amino acids that feed two bacterial species and could tell us much more about the 90% of bacteria that humans have hardly studied
An artificial intelligence system enables robots to conduct autonomous scientific experiments—as many as 10,000 per day—potentially driving a drastic leap forward in the pace of discovery in areas from medicine to agriculture to environmental science.
Reported today in Nature Microbiology, the team was led by a professor now at the University of Michigan.
That artificial intelligence platform, dubbed BacterAI, mapped the metabolism of two microbes associated with oral health—with no baseline information to start with. Bacteria consume some combination of the 20 amino acids needed to support life, but each species requires specific nutrients to grow. The U-M team wanted to know what amino acids are needed by the beneficial microbes in our mouths so they can promote their growth.
"We know almost nothing about most of the bacteria that influence our health. Understanding how bacteria grow is the first step toward reengineering our microbiome," said Paul Jensen, U-M assistant professor of biomedical engineering who was at the University of Illinois when the project started.
Figuring out the combination of amino acids that bacteria like is tricky, however. Those 20 amino acids yield more than a million possible combinations, just based on whether each amino acid is present or not. Yet BacterAI was able to discover the amino acid requirements for the growth of both Streptococcus gordonii and Streptococcus sanguinis.
To find the right formula for each species, BacterAI tested hundreds of combinations of amino acids per day, honing its focus and changing combinations each morning based on the previous day's results. Within nine days, it was producing accurate predictions 90% of the time.
Unlike conventional approaches that feed labeled data sets into a machine-learning model, BacterAI creates its own data set through a series of experiments. By analyzing the results of previous trials, it comes up with predictions of what new experiments might give it the most information. As a result, it figured out most of the rules for feeding bacteria with fewer than 4,000 experiments.
"When a child learns to walk, they don’t just watch adults walk and then say 'Ok, I got it,' stand up, and start walking. They fumble around and do some trial and error first," Jensen said.
"We wanted our AI agent to take steps and fall down, to come up with its own ideas and make mistakes. Every day, it gets a little better, a little smarter."
Little to no research has been conducted on roughly 90% of bacteria, and the amount of time and resources needed to learn even basic scientific information about them using conventional methods is daunting. Automated experimentation can drastically speed up these discoveries. The team ran up to 10,000 experiments in a single day.
But the applications go beyond microbiology. Researchers in any field can set up questions as puzzles for AI to solve through this kind of trial and error.
"With the recent explosion of mainstream AI over the last several months, many people are uncertain about what it will bring in the future, both positive and negative," said Adam Dama, a former engineer in the Jensen Lab and lead author of the study. "But to me, it's very clear that focused applications of AI like our project will accelerate everyday research."
The research was funded by the National Institutes of Health with support from NVIDIA.
May 4, 2023, TORONTO – Ontario research teams investigating new ways to treat cancer are taking the crucial next steps to bring their discoveries to patients thanks to support from the Ontario Institute for Cancer Research (OICR).
OICR announced it is funding five Ontario-based drug discovery projects between $150,000 and $300,000 per project through its Cancer Therapeutics Innovation Pipeline (CTIP) initiative. CTIP supports research into promising molecules that could become the next generation of cancer therapeutics.
This year’s cohort of CTIP projects aims to develop treatments for some of the most devastating cancers, including pancreatic cancer, ovarian cancer, breast cancer and late-stage prostate cancer.
Beyond funding, CTIP’s committee of experts from academia and industry advises research teams on the science and approaches needed to advance their discoveries, and the strategy to attract the partnerships and investments needed to bring a new drug to the clinic.
“Ontario is home to many talented drug discovery researchers, and OICR created CTIP to guide them through the challenges of the drug discovery process,” says OICR President and Scientific Director, Dr. Laszlo Radvanyi. “These exciting new projects have the potential to make a major difference in the lives of people with cancer, and we want to help realize that potential as soon and as impactfully as possible.”
The 2023 CTIP projects include:
Early Validation Projects
Dr. Fred Dick of Western University and Lawson Health Research Institute is investigating new therapies to treat ovarian cancer more effectively by targeting ‘dormant’ cancer cells. Ovarian cancers are usually treated by chemotherapy, but ovarian cancer cells can survive treatment by entering a period of dormancy and then spreading again once treatment is done. Dick and colleagues have uncovered a process that keeps dormant cells alive and will use OICR support to look for ways to disrupt this survival mechanism.
“By going after ‘dormant’ cancer cells that elude the usual treatment options, we aim to prevent ovarian cancer from returning, and stop it once and for all.” – Dr. Fred Dick
Dr. Richard Austin and Dr. Bobby Shayegan from McMaster University and St. Joseph's Healthcare Hamilton are hoping to develop a new drug for prostate cancer that is effective against late-stage disease, when it is usually the hardest to treat. Austin, Shayegan and colleagues have created a synthetic antibody that targets a protein on the surface of prostate cancer cells, which plays a key role in the growth of tumours. After demonstrating promising results of shrinking tumours in mice, they will use CTIP funding to take the next steps toward advancing this potentially first-in-class therapeutic.
“Once prostate cancer spreads, it becomes much more difficult to treat. But we have found an exciting new way to attack prostate cancer cells that could provide new hope to countless men.” – Dr. Richard Austin
Early Accelerator Projects
Dr. Razqallah Hakem and Dr. Mark Reed of the University Health Network are exploring new ways to treat breast and ovarian cancer patients with BRCA1 and BRCA2 gene mutations. These mutations can restrain the body from repairing damaged DNA, which makes people more susceptible to developing cancer. Although patients with these mutations initially respond well to current therapeutic strategies, they develop resistance and recurrence of their tumours. Hakem and Reed identified a novel factor essential for cancer cell survival. They will use OICR funding to test about 25,000 molecules with the goal of identifying those that inhibit their novel factor and kill cancer cells.
“Cancer is stubbornly good at resisting treatments, so it’s crucial to keep innovating. The approach we’re exploring could provide transformative new options for people with BRCA-mutant breast and ovarian cancers.” – Dr. Razqallah Hakem
Dr. Grant Brown of the University of Toronto and Dr. Rima Al-awar of OICR are looking for ways to maximize the effects of gemcitabine, a chemotherapy and one of the few treatments that is effective against pancreatic cancer. Brown and colleagues have found that ‘deactivating’ two genes makes gemcitabine kill pancreatic cancer cells more effectively. With their CTIP award, they will look for chemicals that inhibit the proteins made by these genes in the hopes of finding drugs that can be paired with gemcitabine to more effectively treat one of the deadliest forms of cancer.
“Pancreatic cancer moves quickly, so we need to harness all the tools we have to stop it. Our work aims to take one of the best treatment options for pancreatic cancer to the next level.” – Dr. Grant Brown
Dr. Michael Olson, Dr. Marc Adler and Dr. Russell Viirre of Toronto Metropolitan University are investigating alternative treatments for ovarian cancer that has spread to other parts of the body. Only 20-30 per cent of women survive ovarian cancer when it is diagnosed in the late stages, and treatment options are limited in those stages. But researchers have discovered that disrupting the activity of a particular protein has the potential to kill ovarian cancer cells throughout the body. Thanks to support from OICR, Olson and colleagues will test how tumours respond when the protein is inhibited using three-dimensional ‘patient-derived organoids’ and work to develop new compounds that target the protein.
“Women with advanced ovarian cancer need improved treatment options against this really difficult disease. We hope our unique approach can help deliver an alternative treatment that gives them a better chance of living – and living well.” – Dr. Michael Olson
Including these projects, CTIP has now funded 26 studies since the program launched in 2017.
“The Ontario government is proud to support ground-breaking research that can advance new discoveries and innovation in cancer research,” said Jill Dunlop, Minister of Colleges and Universities. “The initiatives funded by OICR’s Cancer Therapeutics Innovation Pipeline are key to developing new drugs and treatments that have the potential to help patients who are battling cancer lead longer and healthier lives.”
OICR is a collaborative, not-for-profit research institute funded by the Government of Ontario. We conduct and enable high-impact translational cancer research to accelerate the development of discoveries for patients around the world while maximizing the economic benefit of this research for the people of Ontario. For more information visit http://www.oicr.on.ca.
The views expressed are those of OICR and do not necessarily reflect the views of the Province of Ontario.
Pusan National University researchers develop high-adsorption phosphates for radionuclide cesium ion capture
These phosphates present the highest-record adsorption for cesium, higher than any other standard adsorbent
IMAGE: REMOVAL OF THE TOXIC RADIONUCLIDE CS+ FROM NUCLEAR POWER-PLANT WASTES IS A HUGE CHALLENGE. THUS, THERE IS AN URGENT NEED TO REMOVE CS+, POSSIBLY USING A PHOSPHATE-BASED ADSORBENT. TO THIS END, RESEARCHERS HAVE SYNTHESIZED DITTMARITE-TYPE PHOSPHATES, KMP (KMGPO4⋅H2O) AND NMP (NH4MGPO4⋅H2O), THAT CONTAIN READILY EXCHANGEABLE IONS OWING TO THEIR LAYERED STRUCTURE. THE ION EXCHANGE AND DISSOLUTION-PRECIPITATION ENABLED RECORD-HIGH ADSORPTION CAPABILITIES OF KMP AND NMP FOR CS+ THAT ARE HIGHER THAN THOSE OF STANDARD ADSORBENTS.view more
CREDIT: PROF. KUK CHO FROM PUSAN NATIONAL UNIVERSITY
Nuclear energy is crucial for producing cleaner energy, but the associated radioactive pollution requires strategic solutions. Cesium (Cs+) is a toxic radionuclide generated from nuclear power plants that demands immobilization and high adsorption methods to prevent environmental pollution. Although phosphate-based adsorbents are excellent candidates for cleanup, their inefficient ion exchange leads to limited adsorption capacity. The high theoretical adsorption of phosphate adsorbents does not match their experimental adsorption capacities.
To remove harmful Cs+ from radioactive wastewater, Pusan National University researchers led by Professor Kuk Cho from the Department of Environmental Engineering have synthesized dittmarite-type phosphates with a layered structure, ideal for easy ion exchange. The team found that their magnesium phosphates had record-high adsorption capacities for Cs+, surpassing standard adsorbents due to exchangeable ions and dissolution-precipitation. Prof. Cho surmises, "The presence of exchangeable ions and dissolution-precipitation enabled record-high adsorption capacities for Cs+ that are higher than those of standard adsorbents."
The study, which was made available online on April 7, 2023, will be published in Volume 453 of the Journal of Hazardous Materials on 5 July 2023. Using a one-pot hydrothermal method, the team synthesized KMgPO4⋅H2O (KMP) and NH4MgPO4⋅H2O (NMP), both of which are dittmarite-type compounds, having a high theoretical adsorption capacity of 754 mg g− 1 and 856 mg g− 1 for Cs+, respectively. The synthesized KMP and NMP had remarkable adsorption capacities of 630 mg g−1 and 711 mg g−1, respectively, which were 84% of their theoretical adsorption capacities. These experimentally measured adsorption capacity values are the highest among all reported adsorbents for Cs+.
Next, the team characterized and analyzed the physical and chemical properties of the phosphates. Based on the Cs+ adsorption performance of KMP and NMP, they showed that these phosphates are not best suited for use in water with high divalent ion concentrations. However, they can still be used in Cs+ readsorption processes, following desorption processes, to concentrate Cs+ and reduce waste volume. Emphasizing the importance of this, Prof. Cho says, “Cs+ is a popular radionuclide generated from nuclear power plants, and the volume of its waste must be minimized for disposal. To minimize the volume, the adsorbent with higher adsorption capacity is advantageous.”
The study found that the new phosphates efficiently adsorb Cs+, providing a cost-effective method for radioactive waste disposal. This is particularly important in a world where nuclear power plants are expected to increase in number, and proper storage with appropriate adsorbents will become crucial for sustainability.
In conclusion, the high adsorption capacities and stability of the synthesized phosphates make them promising candidates to deal with the radioactive waste disposal challenge.
Pusan National University, located in Busan, South Korea, was founded in 1946, and is now the no. 1 national university of South Korea in research and educational competency. The multi-campus university also has other smaller campuses in Yangsan, Miryang, and Ami. The university prides itself on the principles of truth, freedom, and service, and has approximately 30,000 students, 1200 professors, and 750 faculty members. The university is composed of 14 colleges (schools) and one independent division, with 103 departments in all.
Prof. Kuk Cho is a Professor of Environmental Engineering at the Pusan National University. His group is developing approaches to separate metal ions including radionuclides and heavy metals from water through designing functional materials. His group also focuses on the toxicity of atmospheric particulate matter. Before joining Pusan National University, he completed his postdoctoral training at University of Maryland, College Park. In 2005, Prof. Cho received a PhD in Environmental Engineering from Washington University in St. Louis.
