In addition to its physical effects, Alzheimer’s causes psychological, social and economic ramifications not only for the people living with the disease, but also for those who love and care for them. Because its symptoms worsen over time, it is important for both patients and their caregivers to prepare for the eventual need to increase the amount of support as the disease progresses.

To that end, researchers at The University of Texas at Arlington have created a novel learning-based framework that will help Alzheimer’s patients accurately pinpoint where they are within the disease-development spectrum. This will allow them to best predict the timing of the later stages, making it easier to plan for future care as the disease advances.

“For decades, a variety of predictive approaches have been proposed and evaluated in terms of the predictive capability for Alzheimer’s disease and its precursor, mild cognitive impairment,” said Dajiang Zhu, an associate professor in computer science and engineering at UTA. He is lead author on a new peer-reviewed paper published open access in Pharmacological Research. “Many of these earlier prediction tools overlooked the continuous nature of how Alzheimer’s disease develops and the transition stages of the disease.”

In work supported by more than $2 million in grants from the National Institutes of Health and the National Institute on Aging, Zhu’s Medical Imaging and Neuroscientific Discovery research lab and Li Wang, UTA associate professor in mathematics, developed a new learning-based embedding framework that codes the various stages of Alzheimer’s disease development in a process they call a “disease-embedding tree,” or DETree. Using this framework, the DETree can not only predict any of the five fine-grained clinical groups of Alzheimer’s disease development efficiently and accurately but can also provide more in-depth status information by projecting where within it the patient will be as the disease progresses.

To test their DETree framework, the researchers used data from 266 individuals with Alzheimer’s disease from the multicenter Alzheimer’s Disease Neuroimaging Initiative. The DETree strategy results were compared with other widely used methods for predicting Alzheimer’s disease progression, and the experiment was repeated several times using machine learning-methods to validate the technique.

“We know individuals living with Alzheimer’s disease often develop worsening symptoms at very different rates,” Zhu said. “We’re heartened that our new framework is more accurate than the other prediction models available, which we hope will help patients and their families better plan for the uncertainties of this complicated and devastating disease.”

He and his team believe that the DETree framework has the potential to help predict the progression of other diseases that have multiple clinical stages of development, such as Parkinson’s disease, Huntington’s disease, and Creutzfeldt-Jakob disease.

In new research published in the journal Science Advances, a team led by UCLA and The University of Oslo shows that at some point, the crater filled with water, depositing layers of sediments on the crater floor. The lake subsequently shrank and sediments carried by the river that fed it formed an enormous delta. As the lake dissipated over time, the sediments in the crater were eroded, forming the geologic features visible on the surface today.

The periods of deposition and erosion took place over eons of environmental changes, the radar indicates, confirming that inferences about the Jezero crater’s geologic history based on Mars images obtained from space are accurate.

“From orbit we can see a bunch of different deposits, but we can’t tell for sure if what we’re seeing is their original state, or if we’re seeing the conclusion of a long geological story,” said David Paige, a UCLA professor of Earth, planetary and space sciences and first author of the paper. “To tell how these things formed, we need to see below the surface.”

The rover, which is about the size of a car and carries seven scientific instruments, has been exploring the 30-mile-wide crater, studying its geology and atmosphere and collecting samples since 2021. Perseverance’s soil and rock samples will be brought back to Earth by a future expedition and studied for evidence of past life.

Between May and December 2022, Perseverance drove from the crater floor onto the delta, a vast expanse of 3 billion-year-old sediments that, from orbit, resembles the river deltas on Earth.

As the rover drove onto the delta, Perseverance’s Radar Imager for Mars’ Subsurface Experiment, or RIMFAX, instrument fired radar waves downward at 10-centimeter intervals and measured pulses reflected from depths of about 20 meters below the surface. With the radar, scientists can see down to the base of the sediments to reveal the top surface of the buried crater floor.

Years of research with ground-penetrating radar and testing of RIMFAX on Earth have taught scientists how to read the structure and composition of subsurface layers from their radar reflections. The resulting subsurface image shows rock layers that can be interpreted like a highway road cut.

“Some geologists say that the ability of radar to see under the surface is kind of like cheating,” said Paige, who is RIMFAX’s deputy principal investigator.

RIMFAX imaging revealed two distinct periods of sediment deposition sandwiched between two periods of erosion. UCLA and the University of Oslo report that the crater floor below the delta is not uniformly flat, suggesting that a period of erosion occurred prior to the deposition of lake sediments. The radar images show that the sediments are regular and horizontal — just like sediments deposited in lakes on Earth. The existence of lake sediments had been suspected in previous studies, but has been confirmed by this research.

A second period of deposition occurred when fluctuations in the lake level allowed the river to deposit a broad delta that once extended far out into the lake, but has now eroded back closer to the river’s mouth.

“The changes we see preserved in the rock record are driven by large-scale changes in the Martian environment,” Paige said. “It’s cool that we can see so much evidence of change in such a small geographic area, which allows us extend our findings to the scale of the entire crater.”

“Hematopoietic stem cells, or HSCs, produce blood and immune cells, but not all HSCs are equally productive,” said the study’s corresponding author Rong Lu, PhD, who is an associate professor of stem cell biology and regenerative medicine, biomedical engineering, medicine, and gerontology at USC, and a Leukemia & Lymphoma Society Scholar. “We wanted to understand the mechanism of why some stem cells produce more immune cells, while other stem cells produce fewer.”

With this goal in mind, first author Du Jiang, PhD, and his colleagues in the in the Lu Lab at the Keck School of Medicine of USC pioneered new techniques for understanding the quantitative association between immune cell production and gene expression in lab mice. The scientists labeled individual stem cells with genetic “barcodes” to track their immune cell production. They then correlated the barcode tracking with measurements of gene expression activity. They also developed innovative bioinformatics approaches to characterize their quantitative association.

By leveraging these technical advances, the scientists identified nearly 40 genes — including genes associated with diseases such as myelodysplastic syndrome, a type of cancer caused by abnormal blood-forming cells — that are related to immune cell production. They discovered associations between the activity of these genes and both the quantity and variety of immune cells produced. For example, certain genes are associated with the production of lymphoid cells, others with myeloid cells, and still others with a healthy balance of various immune cell types.

A few of the genes showed what the scientists described as a “constant association” with the production of lymphocytes only. In other words, at any level of lymphocyte output, gene expression was always associated with lymphocyte production.

A few other genes had a “discrete association” with the production of lymphocytes only. This means that gene activity was associated with lymphocyte production within a specific range of lymphocyte output levels.

Most commonly, genes would have either a “unimodal or multimodal” association with immune cell production. In these instances, which involved both lymphoid and myeloid cells, gene activity was only associated with immune cell production at either one or multiple specific levels of immune cell production.

“In this study, we show that most genes associated with immune cell production are associated only at specific levels of immune cell production,” said Jiang, who earned his PhD in the Lu Lab.”Our findings can inform strategies to optimize bone marrow transplantation — for example, by selecting donor bone marrow cells with gene activity associated with high and balanced levels of immune cell production.”

Additional authors include Adnan Y. Chowdhury, Anna Nogalska, Jorge Contreras, Yeachan Lee, Mary Vergel-Rodriguez, and Melissa Valenzuela from the Lu Lab.

The project was supported by federal funding from the National Institutes of Health (grants R00HL113104, R01HL138225, R01HL135292, R01HL135292-S1, R35HL150826, and R01AG080982) and the National Cancer Institute (grant P30CA014089). Additional support came from the Leukemia & Lymphoma Society (grant LLS-1370-20), California Institute for Regenerative Medicine, and the Hearst Foundations.