Tyler O'Neal, Staff Editor ACADEMIA

Massachusetts General Hospital uses deep learning models to identify people at risk of thoracic aortic aneurysm

The results could also lead to new strategies to prevent and treat enlarged aortas.

An abnormally enlarged aorta—also called aortic aneurysm—can tear or rupture and cause sudden cardiac death. Unfortunately, patients often show no signs or symptoms before the aorta, which carries blood from the heart to the rest of the body, fails. A team led by investigators at Massachusetts General Hospital (MGH) recently used a type of artificial intelligence called deep learning to uncover insights into the genetic basis for variation in the aorta’s size. In addition to identifying at-risk individuals, the findings may point to new preventive and therapeutic targets.

The research relied on data from the UK Biobank, a study that performed multiple magnetic resonance imaging tests of the heart and aorta in more than 40,000 people. “There were no aortic measurements provided by the UK Biobank, and we wanted to read the aortic diameter in all of the images collected,” explains lead author James Pirruccello, MD, a cardiologist at MGH and an instructor in medicine at Harvard Medical School. “That is very hard for a human to do because it would take a long time, which motivated our use of deep learning models to do this process at a large scale.”

The researchers trained deep learning models to evaluate the dimensions of the ascending and descending sections of the aorta in 4.6 million cardiac images. They then analyzed the study participants’ genes to identify variations in 82 genetic regions (or loci) linked to the diameter of the ascending aorta and 47 linked to the diameter of the descending aorta. Some of the loci were near genes with known associations with aortic disease.

“When we added up the genetic variants into what’s called a polygenic score, people with a higher score were more likely to be diagnosed with aortic aneurysm by a doctor,” says Pirruccello. “This suggests that, after further development and testing, such a score might one day be useful to help us identify people at high risk of an aneurysm. The genetic loci that we discovered also offer a useful starting point for trying to identify new drug targets for aortic enlargement.”

Pirruccello adds that the findings also provide supportive evidence that deep learning and other machine learning methods can help accelerate scientific analyses of complex biomedical data such as imaging results.

This work was supported by Leducq, the National Institutes of Health, the American Heart Association, the John S. LaDue Memorial Fellowship, a Sarnoff Cardiovascular Research Foundation Scholar Award, the Burroughs Wellcome Fund, the Fredman Fellowship for Aortic Disease, the Toomey Fund for Aortic Dissection Research, Bayer AG, and the Susan Eid Tumor Heterogeneity Initiative.

Vanderbilt engineer Kolouri wins $1M DARPA grant to investigate AI cooperative lifelong learning

Tyler O'Neal, Staff Editor ACADEMIA

A Vanderbilt engineering professor is leading part of an international initiative to create advanced artificial intelligence programs that will enable machines to learn progressively over a lifetime and share those experiences. Researchers hope the technology will allow machines to reuse information, adapt quickly to new conditions, and collaborate by sharing information. Soheil Kolouri

Soheil Kolouri, assistant professor of computer science, in partnership with Hamed Pirsiavash, associate professor of computer science at the University of California, Davis, will lead a research team focusing on continual machine learning mechanisms.

The prototype project, “Information Distillation for Embodied and Articulate Lifelong Learners,” or IDEALL, has received a $1M award from the Defense Advanced Research Projects Agency as part of the agency’s Shared-Experience Lifelong Learning (ShELL) initiative. DARPA wants to develop AI agents that share their experiences and is seeking innovative basic or applied research concepts in lifelong learning.

In addition to leading IDEALL, Kolouri’s team has partnered with Andrea Soltoggio, associate professor of computer science, University of Loughborough, UK, to develop a theoretical framework that allows AI agents to measure tasks’ similarities and continually learn by analogies. Cong Liu, associate professor of computer science at, University of Texas, Dallas, is a member of the Loughborough team.  

The Vanderbilt-UC, Davis team will concentrate on the algorithmic theory and statistical foundation of the learning mechanisms. The UK team will focus on novel bio-inspired neural networks that learn shareable knowledge exploiting neuromodulation and synaptic consolidation mechanisms, and the Texas researchers will focus on the hardware integration and deployment for potential transition to industrial and real-world applications.

The real-world uses of this new technology could include cooperating self-learning autonomous vehicles such as self-driving cars, robotic rescue and exploration systems, distributed monitoring systems to detect emergencies, or cyber security systems of agents that monitor large networks.

Lifelong Learning is a relatively new area of machine learning research in which agents continually learn as they encounter varying conditions and tasks while deployed in the field, acquiring experience and knowledge and improving performance on both novel and previous tasks. This differs from the train-then-deploy process for typical ML systems.

LL is an emerging area of machine learning that differs from the traditional train and then deploy process. In LL, an AI agent must continually learn from the input data stream while preserving and improving its previously acquired knowledge.

“Lifelong learning from the never-ending stream of everchanging data is the key to scaling up AI systems,” said Kolouri. “One of the major roadblocks in achieving LL is the so-called plasticity-stability trade-off, where plasticity refers to the ability to learn from new data, and stability refers to retaining the previously learned knowledge.” A team of undergraduate and graduate students at the Machine Intelligence and Neural Technologies (MINT) Lab directed by Kolouri, in collaboration with computer science associate professor Vladimir Braverman’s group at Johns Hopkins University, is currently studying this phenomenon.

“Today we know the importance of social interactions in the evolution of human intelligence. Artificial General Intelligence (AGI) could not be realized with a single AI agent. Similar to the cognitive revolution in sapiens, a transition is needed from our current single-agent LL to articulate LL machines that can encode information about their surroundings into a compact compositional language and use it for machine-to-machine communication and, maybe more importantly, for thinking, which is a form of self-communication!” said Kolouri. “The ShELL program aims to develop such communicative LL agents that continually learn from their collective experiences.”

“We are very excited to be part of this fast-paced, innovative program and look forward to transitioning our developed tools into medical applications,” Kolouri said.

German scientists pave the way for superconducting spintronic apps where quantum coherence protects spin polarized current flow

Tyler O'Neal, Staff Editor ACADEMIA

Superconducting coupling between two regions separated by a one-micron wide ferromagnetic compound has been proved by an international team. This macroscopic quantum effect, known as Josephson effect, generates an electrical current within the ferromagnetic compound made of superconducting Cooper-pairs. Magnetic imaging of the ferromagnetic region at BESSY II has contributed to demonstrating that the spin of the electrons forming the Cooper pairs is equal. These results pave the way for low-power consumption superconducting spintronic-applications where spin-polarized currents can be protected by quantum coherence. Device where the long range Josephson coupling has been demonstrated. Superconducting YBa2Cu3O7 regions (yellow) are separated by a half-metal La2/3Sr1/3MnO3 ferromagnet (green).

When two superconducting regions are separated by a strip of non-superconducting material, a special quantum effect can occur, coupling both regions: The Josephson effect. If the spacer material is a half-metal ferromagnet novel implications for spintronic applications arise. An international team has now for the first time designed a material system that exhibits an unusually long-range Josephson effect: Here, regions of superconducting YBa2Cu3O7 are separated by a region of half-metallic, ferromagnetic manganite (La2/3Sr1/3MnO3) one micron wide.

With the help of magneto-transport measurements, the researchers were able to demonstrate the presence of a supercurrent circulating through the manganite – this supercurrent is arising from the superconducting coupling between both superconducting regions, and thus a manifestation of a Josephson effect with a macroscopic long range.

Extremely rare: Triplett superconductivity

In addition, the scientists explored another interesting property with profound consequences for spintronic applications. In superconductors electrons pair together in so-called Cooper pairs. In the vast majority of superconducting materials, these pairs are composed of electrons with opposite spins to minimize the magnetic exchange field which is detrimental for the stabilization of superconductivity. The ferromagnet used by the international team has been a half-ferromagnet for which only one spin-type electron is allowed to circulate. The fact that a supercurrent has been detected within this material, implies that the Cooper pairs of this supercurrent must be composed of electrons having the same spin. This so-called “triplet” superconductivity is extremely rare.

Mapping magnetic domains at BESSY II

"At the XMCD-PEEM station at BESSY II, we mapped and measured the magnetic domains within the manganite spacer. We observed wide regions homogeneously magnetized and connecting the superconducting regions. Triplet spin pairs can propagate freely in these,” explains Dr. Sergio Valencia Molina, HZB physicist, who supervised the measurements at BESSY II. 

Superconducting currents flow without resistance which makes them very appealing for low-power consumption applications. In the present case, this current is made of electrons with equal spins. Such spin-polarized currents could be used in novel superconducting spintronic applications for the transport (over long distances) and reading/writing of information while profiting from the stability imposed by the macroscopic quantum coherence of the Josephson effect.

The new device made of the superconducting and ferromagnetic components, therefore, opens up opportunities for superconducting spintronics and new perspectives for quantum supercomputing.