Immune cell appetizers for cancer treatment 

Our immune system contains white blood cells called macrophages that perform phagocytosis, a process where they engulf or “eat” unwanted debris like microorganisms, dead cells and cancer cells. Assistant Professor Meghan Morrissey of the Department of Molecular, Cellular, and Developmental Biology and researchers in the Morrissey Lab recently published a study exploring the appetite of macrophages and how they might be stimulated to be more “hungry.” Understanding this mechanism could lead to more effective cancer therapies. 

In order to identify which cells are healthy and which should be eaten, macrophages seek a specific antibody called immunoglobulin G (IgG), which is recognized by Fc receptors on the surface of the macrophage cell. When enough Fc receptors become activated by IgG, it reaches a certain threshold and the macrophage decides to eat. 

Researchers in the Morrissey Lab created an Fc receptor that is activated by blue light instead of IgG, allowing them to control the activity of Fc receptors. This tool enabled them to stimulate macrophage appetite, increasing their consumption of artificial cancer cells. They found that macrophages exposed to mild Fc receptor activation, or what we call an appetizer, ate more cancer cells than those who had no subthreshold stimulus. This can lead to short-term priming of macrophages, where an appetizer increases Fc receptor mobility to more easily assemble when encountering IgG. It also contributes to long-term priming, which requires making new proteins to stimulate appetite for a longer period of time.

The results suggest that macrophage cancer therapy might be more effective when macrophages are primed with an Immunoglobulin G appetizer. This also suggests that macrophages, part of the innate immune system, have a mechanism for immunological memory, a trait that scientists until recently had only observed in the adaptive adaptive immune system. Immunological memory is the ability of the immune system to respond more quickly and effectively to pathogens it has already encountered. To better understand macrophage phagocytosis and its potential for cancer therapy, the lab intends to explore whether the IgG priming mechanism varies among different tissues and whether priming can be successful in human tissues.

Mosquitoes and thermal infrared

Mosquitoes use various senses to seek out their hosts and find a meal from an unsuspecting human, such as detecting carbon dioxide from our breath and odors from our skin. A recent discovery by the Craig Montell Lab at UC Santa Barbara revealed that mosquitoes can also detect thermal infrared (IR) radiation from our body heat. Mosquito-borne diseases are prevalent among half the world’s population, and understanding how they find their hosts might help to mitigate rising mosquito populations and death related to the diseases they transmit.

To study whether mosquitoes use IR to detect hosts, mosquitoes were placed in a cage with human odors and carbon dioxide, but only one area contained a thermal IR source set at human skin temperature. Researchers discovered that thermal IR was used by mosquitoes to detect humans up to 70 centimeters, or 2.5 feet away, and that host detection doubled in the presence of a thermal IR source. They also found that IR alone, in the absence of signals like carbon dioxide and human odor, made no difference in mosquito host detection. This indicates that multiple signals must be present for a mosquito to seek out the host. 

In addition, researchers explored how mosquitoes are able to detect IR. Previous research has shown that mosquitoes’ antennae contain heat-sensing neurons and a protein called TRPA1 that allows them to detect temperature. Montell and his lab found that if mosquitoes lack the TRPA1 gene, they cannot sense IR. However, this protein alone is not fully responsible for the mosquitoes’ ability to sense IR from a greater distance (2.5 feet). The researchers discovered that specific rhodopsins, proteins involved in light detection, taste and temperature sensing, contribute to detecting IR at lower levels.

Understanding how thermal IR is a strong stimulus for mosquito host detection can lead to more efficient methods of controlling the growth of mosquito populations. For example, mosquito traps might utilize thermal IR sources to attract them more efficiently. Additionally, this discovery opens up the door to developing other strategies to block mosquito detection of thermal IR and prevent mosquito-human interactions. 

Complex computing

Researchers at the UCSB Strukov Research Group recently published a paper proposing new ways of improving how computers handle complex math and computing. As computers face more complex tasks, such as artificial intelligence (AI) training and optimization and improving machine accuracy, the need for more efficient hardware for complex computing is growing. Traditional computing systems are facing challenges when it comes to solving complex mathematics, but the researchers proposed a new method that allows computation of gradients for high-degree polynomials.

Memristors, or memory elements that combine storage and computing functions, were utilized in the hardware because they are able to reduce the time and energy spent moving data between memory and processors. This allows for more efficient computation. 

Researchers in the Strukov Group proposed two approaches — one focusing on binary-variable polynomials and the other, a broader method, involving general polynomial functions. Binary-variable polynomials are common in optimization and have variables restricted to “yes” or “no,” or “0” or “1.” General polynomials can handle more types of equations whose variables have actual number values but require more complex hardware. 

Researchers were able to build a hardware prototype for the binary-variable approach that was successful in computing gradients much faster and more efficiently than traditional systems. When testing the hardware with larger simulations and more practical problems, it again showed a clear dominance in speed and energy efficiency. This suggests that the binary-variable approach is an advancement in complex computations and a step in creating hardware that can handle tasks like optimization and AI, including machine learning. This opens up new opportunities to improve decision making, problem solving and efficiency in AI applications.

A version of this article appeared on p.15 of the Oct. 3, 2024 edition of the Daily Nexus.