In the study, published March 8 in Circulation Research, the researchers showed that boosting levels of a sphingolipid called S1P in artery-lining endothelial cells slows the development and progression of coronary artery disease in an animal model. The lead author was Dr. Onorina Laura Manzo, a postdoctoral researcher in the laboratory of Dr. Annarita Di Lorenzo, an associate professor of pathology and laboratory medicine at Weill Cornell Medicine.

Sphingolipids are named for the enigmatic sphinx of ancient mythology because their functions in biology traditionally have been somewhat mysterious. In recent years, there has been increasing evidence of their relevance in coronary artery disease; bloodstream levels of S1P, for example, are lower in patients with this condition. But the precise roles of these lipids have remained unclear.

In the new study, the researchers sought a better understanding of those roles — and of sphingolipids’ potential as therapeutic targets. Despite the availability of cholesterol-lowering drugs and other interventions, coronary artery disease — the underlying cause of most heart attacks and many strokes — continues to be the world’s leading cause of mortality, affecting more than 20 million people in the United States alone.

Using a novel mouse model developed by the same group, the researchers found that blood pressure-related stress on arteries — which eventually will induce coronary artery disease — triggers an increase in S1P production in endothelial cells, as part of a protective response. This response normally is only temporary, but deleting a protein called NOGO-B, which inhibits S1P production, allows the rise in endothelial S1P production to be sustained — and made the animals much more resistant to coronary artery disease and associated mortality.

Another key finding is related to a different group of sphingolipids called ceramides. Prior studies have linked coronary artery disease to high bloodstream levels of some ceramides, and their causative role in the disease has been widely assumed. In their model, however, the researchers observed that while ceramide levels were high in the bloodstream, levels in artery-lining endothelial cells remained about the same regardless of coronary artery disease status. This suggests that the current view of ceramides’ role in the disease should be revised.

All in all, the findings lay the foundation for the development of drugs that boost S1P to treat or prevent coronary artery disease, the researchers concluded.

The work reported in this story was supported by the National Heart, Lung, and Blood Institute, part of the National Institutes of Health, through grant numbers R01HL126913 and R01HL152195 and a Harold S. Geneen Charitable Trust Award for Coronary Heart Disease Research.

“We looked at how the rates of fluid production with oil and gas compare to natural background circulation of water and showed how humans have made a big impact on the circulation of fluids in the subsurface,” said Jennifer McIntosh, a professor in the UArizona Department of Hydrology and Atmospheric Sciences and senior author of a paper in the journal Earth’s Future detailing the findings.

“The deep subsurface is out of sight and out of mind for most people, and we thought it was important to provide some context to these proposed activities, especially when it comes to our environmental impacts,” said lead study author Grant Ferguson, an adjunct professor in the UArizona Department of Hydrology and Atmospheric Sciences and a professor in the University of Saskatchewan’s School of Environment and Sustainability.

In the future, these human-induced fluid fluxes are projected to increase with strategies that are proposed as solutions for climate change, according the study. Such strategies include: geologic carbon sequestration, which is capturing and storing atmospheric carbon dioxide in underground porous rocks; geothermal energy production, which involves circulating water through hot rocks for generating electricity; and lithium extraction from underground mineral-rich brine for powering electric vehicles. The study was done in collaboration with researchers from the University of Saskatchewan in Canada, Harvard University, Northwestern University, the Korea Institute of Geosciences and Mineral Resources, and Linnaeus University in Sweden.

“Responsible management of the subsurface is central to any hope for a green transition, sustainable future and keeping warming below a few degrees,” said Peter Reiners, a professor in the UArizona Department of Geosciences and a co-author of the study.

With oil and natural gas production, there is always some amount of water, typically saline, that comes from the deep subsurface, McIntosh said. The underground water is often millions of years old and acquires its salinity either from evaporation of ancient seawater or from reaction with rocks and minerals. For more efficient oil recovery, more water from near-surface sources is added to the salt water to make up for the amount of oil removed and to maintain reservoir pressures. The blended saline water then gets reinjected into the subsurface. This becomes a cycle of producing fluid and reinjecting it to the deep subsurface.

The same process happens in lithium extraction, geothermal energy production and geologic carbon sequestration, the operations of which involve leftover saline water from the underground that is reinjected.

“We show that the fluid injection rates or recharge rates from those oil and gas activities is greater than what naturally occurs,” McIntosh said.

Using existing data from various sources, including measurements of fluid movements related to oil and gas extraction and water injections for geothermal energy, the team found that the current fluid movement rates induced by human activities are higher compared to how fluids moved before human intervention.

As human activities like carbon capture and sequestration and lithium extraction ramp up, the researchers also predicted how these activities might be recorded in the geological record, which is the history of Earth as recorded in the rocks that make up its crust.

Human activities have the potential to alter not just the deep subsurface fluids but also the microbes that live down there, McIntosh said. As fluids move around, microbial environments may be altered by changes in water chemistry or by bringing new microbial communities from Earth’s surface to the underground.

For example, with hydraulic fracturing, a technique that is used to break underground rocks with pressurized liquids for extracting oil and gas, a deep rock formation that previously didn’t have any detectable number of microbes might have a sudden bloom of microbial activity.

There remain a lot of unknowns about Earth’s deep subsurface and how it is impacted by human activities, and it’s important to continue working on those questions, McIntosh said.

“We need to use the deep subsurface as part of the solution for the climate crisis,” McIntosh said. “Yet, we know more about the surface of Mars than we do about water, rocks and life deep beneath our feet.”

To design these climate-saving plants, scientists in Salk’s Harnessing Plants Initiative are using a sophisticated new research tool called SLEAP — an easy-to-use artificial intelligence (AI) software that tracks multiple features of root growth. Created by Salk Fellow Talmo Pereira, SLEAP was initially designed to track animal movement in the lab. Now, Pereira has teamed up with plant scientist and Salk colleague Professor Wolfgang Busch to apply SLEAP to plants.

In a study published in Plant Phenomics on April 12, 2024, Busch and Pereira debut a new protocol for using SLEAP to analyze plant root phenotypes — how deep and wide they grow, how massive their root systems become, and other physical qualities that, prior to SLEAP, were tedious to measure. The application of SLEAP to plants has already enabled researchers to establish the most extensive catalog of plant root system phenotypes to date.

What’s more, tracking these physical root system characteristics helps scientists find genes affiliated with those characteristics, as well as whether multiple root characteristics are determined by the same genes or independently. This allows the Salk team to determine what genes are most beneficial to their plant designs.

“This collaboration is truly a testament to what makes Salk science so special and impactful,” says Pereira. “We’re not just ‘borrowing’ from different disciplines — we’re really putting them on equal footing in order to create something greater than the sum of its parts.”

Prior to using SLEAP, tracking the physical characteristics of both plants and animals required a lot of labor that slowed the scientific process. If researchers wanted to analyze an image of a plant, they would need to manually flag the parts of the image that were and weren’t plant — frame-by-frame, part-by-part, pixel-by-pixel. Only then could older AI models be applied to process the image and gather data about the plant’s structure.

What sets SLEAP apart is its unique use of both computer vision (the ability for computers to understand images) and deep learning (an AI approach for training a computer to learn and work like the human brain). This combination allows researchers to process images without moving pixel-by-pixel, instead skipping this intermediate labor-intensive step to jump straight from image input to defined plant features.

“We created a robust protocol validated in multiple plant types that cuts down on analysis time and human error, while emphasizing accessibility and ease-of-use — and it required no changes to the actual SLEAP software,” says first author Elizabeth Berrigan, a bioinformatics analyst in Busch’s lab.

Without modifying the baseline technology of SLEAP, the researchers developed a downloadable toolkit for SLEAP called sleap-roots (available as open-source software here). With sleap-roots, SLEAP can process biological traits of root systems like depth, mass, and angle of growth. The Salk team tested the sleap-roots package in a variety of plants, including crop plants like soybeans, rice, and canola, as well as the model plant species Arabidopsis thaliana — a flowering weed in the mustard family. Across the variety of plants trialed, they found the novel SLEAP-based method outperformed existing practices by annotating 1.5 times faster, training the AI model 10 times faster, and predicting plant structure on new data 10 times faster, all with the same or better accuracy than before.

Together with massive genome sequencing efforts for elucidating the genotype data in large numbers of crop varieties, these phenotypic data, such as a plant’s root system growing especially deep in soil, can be extrapolated to understand the genes responsible for creating that especially deep root system.

This step — connecting phenotype and genotype — is crucial in Salk’s mission to create plants that hold on to more carbon and for longer, as those plants will need root systems designed to be deeper and more robust. Implementing this accurate and efficient software will allow the Harnessing Plants Initiative to connect desirable phenotypes to targetable genes with groundbreaking ease and speed.

“We have already been able to create the most extensive catalogue of plant root system phenotypes to date, which is really accelerating our research to create carbon-capturing plants that fight climate change,” says Busch, the Hess Chair in Plant Science at Salk. “SLEAP has been so easy to apply and use, thanks to Talmo’s professional software design, and it’s going to be an indispensable tool in my lab moving forward.”

Accessibility and reproducibility were at the forefront of Pereira’s mind when creating both SLEAP and sleap-roots. Because the software and sleap-roots toolkit are free to use, the researchers are excited to see how sleap-roots will be used around the world. Already, they have begun discussions with NASA scientists hoping to utilize the tool not only to help guide carbon-sequestering plants on Earth, but also to study plants in space.

At Salk, the collaborative team is not yet ready to disband — they are already embarking on a new challenge of analyzing 3D data with SLEAP. Efforts to refine, expand, and share SLEAP and sleap-roots will continue for years to come, but its use in Salk’s Harnessing Plants Initiative is already accelerating plant designs and helping the Institute make an impact on climate change.

Other authors include Lin Wang, Hannah Carrillo, Kimberly Echegoyen, Mikayla Kappes, Jorge Torres, Angel Ai-Perreira, Erica McCoy, Emily Shane, Charles Copeland, Lauren Ragel, Charidimos Georgousakis, Sanghwa Lee, Dawn Reynolds, Avery Talgo, Juan Gonzalez, Ling Zhang, Ashish Rajurkar, Michel Ruiz, Erin Daniels, Liezl Maree, and Shree Pariyar of Salk.

The work was supported by the Bezos Earth Fund, the Hess Corporation, the TED Audacious Project, and the National Institutes of Health (RF1MH132653).

About 50% of the world’s chocolate originates from cacao trees in the West Africa countries of Ivory Coast and Ghana. The damaging virus is attacking cacao trees in Ghana, resulting in harvest losses of between 15 and 50%. Spread by small insects called mealybugs that eat the leaves, buds and flowers of trees, the cacao swollen shoot virus disease (CSSVD) is among the most damaging threats to the root ingredient of chocolate.

“This virus is a real threat to the global supply of chocolate,” said Benito Chen-Charpentier, professor of mathematics at The University of Texas at Arlington and an author of “Cacao sustainability: The case of cacao swollen-shoot virus co-infection” in the journal PLoS One. “Pesticides don’t work well against mealybugs, leaving farmers to try to prevent the spread of the disease by cutting out infected trees and breeding resistant trees. But despite these efforts, Ghana has lost more than 254 million cacao trees in recent years.”

Farmers can combat the mealybugs by giving vaccines to the trees to inoculate them from the virus. But the vaccines are expensive, especially for low-wage farmers, and vaccinated trees produce a smaller harvest of cacao, compounding the devastation of the virus.

Chen-Charpentier and colleagues from the University of Kansas, Prairie View A&M, the University of South Florida and the Cocoa Research Institute of Ghana have developed a new strategy: using mathematical data to determine how far apart farmers can plant vaccinated trees to prevent mealybugs from jumping from one tree to another and spreading the virus.

“Mealybugs have several ways of movement, including moving from canopy to canopy, being carried by ants or blown by the wind,” Chen-Charpentier said. “What we needed to do was create a model for cacao growers so they could know how far away they could safely plant vaccinated trees from unvaccinated trees in order to prevent the spread of the virus while keeping costs manageable for these small farmers.”

By experimenting with mathematical patterning techniques, the team created two different types of models that allow farmers to create a protective layer of vaccinated cacao trees around unvaccinated trees.

“While still experimental, these models are exciting because they would help farmers protect their crops while helping them achieve a better harvest,” Chen-Charpentier said. “This is good for the farmers’ bottom line, as well as our global addiction to chocolate.”

Engineers often speed things up using hardware that reduces the need to move so much data back and forth. While these machine-learning accelerators can streamline computation, they are susceptible to attackers who can steal secret information.

To reduce this vulnerability, researchers from MIT and the MIT-IBM Watson AI Lab created a machine-learning accelerator that is resistant to the two most common types of attacks. Their chip can keep a user’s health records, financial information, or other sensitive data private while still enabling huge AI models to run efficiently on devices.

The team developed several optimizations that enable strong security while only slightly slowing the device. Moreover, the added security does not impact the accuracy of computations. This machine-learning accelerator could be particularly beneficial for demanding AI applications like augmented and virtual reality or autonomous driving.

While implementing the chip would make a device slightly more expensive and less energy-efficient, that is sometimes a worthwhile price to pay for security, says lead author Maitreyi Ashok, an electrical engineering and computer science (EECS) graduate student at MIT.

“It is important to design with security in mind from the ground up. If you are trying to add even a minimal amount of security after a system has been designed, it is prohibitively expensive. We were able to effectively balance a lot of these tradeoffs during the design phase,” says Ashok.

Her co-authors include Saurav Maji, an EECS graduate student; Xin Zhang and John Cohn of the MIT-IBM Watson AI Lab; and senior author Anantha Chandrakasan, MIT’s chief innovation and strategy officer, dean of the School of Engineering, and the Vannevar Bush Professor of EECS. The research will be presented at the IEEE Custom Integrated Circuits Conference.

Side-channel susceptibility

The researchers targeted a type of machine-learning accelerator called digital in-memory compute. A digital IMC chip performs computations inside a device’s memory, where pieces of a machine-learning model are stored after being moved over from a central server.

The entire model is too big to store on the device, but by breaking it into pieces and reusing those pieces as much as possible, IMC chips reduce the amount of data that must be moved back and forth.

But IMC chips can be susceptible to hackers. In a side-channel attack, a hacker monitors the chip’s power consumption and uses statistical techniques to reverse-engineer data as the chip computes. In a bus-probing attack, the hacker can steal bits of the model and dataset by probing the communication between the accelerator and the off-chip memory.

Digital IMC speeds computation by performing millions of operations at once, but this complexity makes it tough to prevent attacks using traditional security measures, Ashok says.

She and her collaborators took a three-pronged approach to blocking side-channel and bus-probing attacks.

First, they employed a security measure where data in the IMC are split into random pieces. For instance, a bit zero might be split into three bits that still equal zero after a logical operation. The IMC never computes with all pieces in the same operation, so a side-channel attack could never reconstruct the real information.

But for this technique to work, random bits must be added to split the data. Because digital IMC performs millions of operations at once, generating so many random bits would involve too much computing. For their chip, the researchers found a way to simplify computations, making it easier to effectively split data while eliminating the need for random bits.

Second, they prevented bus-probing attacks using a lightweight cipher that encrypts the model stored in off-chip memory. This lightweight cipher only requires simple computations. In addition, they only decrypted the pieces of the model stored on the chip when necessary.

Third, to improve security, they generated the key that decrypts the cipher directly on the chip, rather than moving it back and forth with the model. They generated this unique key from random variations in the chip that are introduced during manufacturing, using what is known as a physically unclonable function.

“Maybe one wire is going to be a little bit thicker than another. We can use these variations to get zeros and ones out of a circuit. For every chip, we can get a random key that should be consistent because these random properties shouldn’t change significantly over time,” Ashok explains.

They reused the memory cells on the chip, leveraging the imperfections in these cells to generate the key. This requires less computation than generating a key from scratch.

“As security has become a critical issue in the design of edge devices, there is a need to develop a complete system stack focusing on secure operation. This work focuses on security for machine-learning workloads and describes a digital processor that uses cross-cutting optimization. It incorporates encrypted data access between memory and processor, approaches to preventing side-channel attacks using randomization, and exploiting variability to generate unique codes. Such designs are going to be critical in future mobile devices,” says Chandrakasan.

Safety testing

To test their chip, the researchers took on the role of hackers and tried to steal secret information using side-channel and bus-probing attacks.

Even after making millions of attempts, they couldn’t reconstruct any real information or extract pieces of the model or dataset. The cipher also remained unbreakable. By contrast, it took only about 5,000 samples to steal information from an unprotected chip.

The addition of security did reduce the energy efficiency of the accelerator, and it also required a larger chip area, which would make it more expensive to fabricate.

The team is planning to explore methods that could reduce the energy consumption and size of their chip in the future, which would make it easier to implement at scale.

“As it becomes too expensive, it becomes harder to convince someone that security is critical. Future work could explore these tradeoffs. Maybe we could make it a little less secure but easier to implement and less expensive,” Ashok says.

The research is funded, in part, by the MIT-IBM Watson AI Lab, the National Science Foundation, and a Mathworks Engineering Fellowship.

The research, published April 16 as a Reviewed Preprint in eLife, is described by editors as an important study with convincing findings of potential interest to the fields of veterinary medicine, public health and epidemiology.

Loss of livestock through abortion is a major concern for the worldwide livestock industry, resulting in significant economic loss and posing a direct threat to public health through transmission of infection. The impact of livestock abortion on the world’s poorest livestock keepers is likely to be substantial — from the direct loss of high-quality food sources and reduced income from sales of milk or meat.

“Effective livestock health surveillance provides critical data for evidence-based approaches to disease control and management, but requires reliable, high-quality and timely data drawn from multiple sources,” says lead author Felix Lankester, Clinical Associate Professor at the Paul G. Allen School for Global Health, Washington State University, Washington, US, and the Global Animal Health Tanzania, Arusha, Tanzania. “Event-based surveillance can detect early events that signal emerging human health risks, and surveillance of livestock abortion events has clear potential for identifying and preventing outbreaks of emerging diseases. However, there is limited information on the current practices, effectiveness and challenges of livestock abortion surveillance, particularly in low and middle-income countries.”

To address this gap, researchers set up a pilot livestock surveillance system in northern Tanzania in 15 wards across five districts, with a mix of pastoral, agropastoral and smallholder livestock keepers. Livestock field officers (LFOs; government employees equivalent to para-veterinarians) received training on the safe investigation of livestock abortion and were requested to report any incidents of abortions, stillbirths and perinatal death. If the cases could be followed up within 72 hours of the abortion event, further investigation including blood, milk and vaginal swabs were collected from the aborting dam, alongside tissue and swab samples from the foetus and placenta. These were tested for a wide range of infectious agents and antibodies.

Between 2017 and 2019, 215 abortion cases were reported from 150 households in 13 of the 15 wards. Of these 215 cases, 70% were reported by three (20%) of the LFOs. Most abortions were investigated within two days, and none were investigated more than four days later. Placental and foetal tissues were only collected in 24% and 34% of cases, respectively, often because these tissues were not found, but vaginal and milk samples were collected in 99% and 78% of cases.

Although data was only available for a limited number of abortions, the results revealed important insights into likely patterns and causes. For example, abortions occurred more often in the dry season, and in non-indigenous cross-bred or exotic animals than in indigenous breeds. More than a fifth of dams that aborted were reported to have experienced a previous abortion, with several experiencing multiple abortion losses, which may suggest that animals suffering recurrent abortion events may have a chronic infection that would warrant their removal from breeding stock or prevent their use as a food source.

The study emphasises the potential risks of exposure to zoonotic pathogens — infectious agents that could potentially jump from livestock to humans. In cases where an infectious agent was detected, 79% were zoonotic, and in nearly a quarter of these cases, someone had assisted with the aborted delivery, likely without any personal protective equipment. Of these, 20% were female and of reproductive age, and therefore of heightened risk from certain pathogens.

“Our study has demonstrated that livestock abortion surveillance, even at a relatively small scale, can capture valuable information about livestock pathogens, including those that are zoonotic,” says senior author Sarah Cleaveland, Professor of Comparative Epidemiology at the School of Biodiversity, One Health & Veterinary Medicine, University of Glasgow, UK. “Moreover, our pilot demonstrates the utility and feasibility of livestock abortion surveillance in rural areas and highlights that engaging field officers, establishing practical and robust field sample collections and ensuring prompt reporting of cases and feedback of results are key elements of effectiveness.”