Optimal culture conditions for cynomolgus monkey naive embryonic stem cells and improved procedures for chimeric embryo culture were developed to allow for high (20%–90%) donor cell contribution to chimeric monkeys.
Alaskans and visitors may be able to see an artificial airglow in the sky created by the High-frequency Active Auroral Research Program during a four-day research campaign that starts Saturday.
Scientists from the University of Alaska Fairbanks, Cornell University, University of Colorado Denver, University of Florida and Georgia Institute of Technology will conduct a variety of experiments at the UAF-operated research site.
The experiments will focus on the ionosphere, the region of the atmosphere between about 30 and 350 miles above the Earth’s surface.
Adoptive cell therapy has emerged as a promising alternative treatment for hematological and solid cancers, with CAR-T therapy standing out as a prominent avenue. In this approach, T cells are genetically engineered with chimeric antigen receptors (CARs) to enhance their targeting capabilities1–2. The outcome of CAR-T cell therapy hinges on a complex interplay of phenotype, activation, and functional profiling of these engineered cells. Immunophenotypic characterization of CAR-T cells assumes a pivotal role in ensuring treatment quality and facilitating continuous monitoring of treatment response1. In the process of immunophenotyping, engineered T cells are separated based on their markers to characterize the composition of the cell population within the sample. The strategic identification and isolation of specific CAR-T cell subsets is essential in augmenting therapy responses2.
Deciphering Cellular Composition, Defining CAR-T Therapy Efficacy
Immunophenotyping is a pivotal technique that combines specific antibodies with fluorescent compounds to reveal specific protein expression in cell populations to identify categorize the tagged cells. Immunophenotyping leverages the differences in surface markers among T cells, reflecting their differentiation, activation, and memory status2. These markers provide insights into immune cell development, function, proliferation potential, and long-term viability. The distinct surface marker profiles closely correlate with the efficacy of CAR-T cell therapy3. Essential markers for immunophenotypic analysis, including CD3, CD4, CD8, CD45RA, CD34R0, CCR7, CD27, and CD95, are presented in Table 1.
“Dinkinesh really did live up to its name; this is marvelous,” said Hal Levison, referring to the meaning of Dinkinesh in the Amharic language, “marvelous.” Levison is principal investigator for Lucy from the Boulder, Colorado, branch of the San-Antonio-based Southwest Research Institute. “When Lucy was originally selected for flight, we planned to fly by seven asteroids. With the addition of Dinkinesh, two Trojan moons, and now this satellite, we’ve turned it up to 11.”
In the weeks prior to the spacecraft’s encounter with Dinkinesh, the Lucy team had wondered if Dinkinesh might be a binary system, given how Lucy’s instruments were seeing the asteroid’s brightness changing with time. The first images from the encounter removed all doubt. Dinkinesh is a close binary. From a preliminary analysis of the first available images, the team estimates that the larger body is approximately 0.5 miles (790 m) at its widest, while the smaller is about 0.15 miles (220 m) in size.
This encounter primarily served as an in-flight test of the spacecraft, specifically focusing on testing the system that allows Lucy to autonomously track an asteroid as it flies past at 10,000 mph, referred to as the terminal tracking system.
NASA’s Lucy Spacecraft took images of asteroid Dinkinesh, discovering that the asteroid has the first-ever contact binary pair orbiting the asteroid.
The resolution of a major discrepancy between theory and experiment for strontium atomic clocks could help improve the precision of these timekeepers.
The low oscillation frequency of spin waves in chromium trichloride enables researchers to explore this antiferromagnet’s rich properties with standard laboratory equipment.
Millimeter-sized “surfers” can self-propel across a vibrating liquid surface, interacting with other surfers to create collective patterns.
Self-propelled objects can move in mesmerizing patterns. The collective movements of groups of such objects typically occur in one of two flow regimes: the inertial regime—think swirling schools of fish in water—or the viscous regime—think swarming colonies of bacteria in mucus. Some self-propelled objects can travel in both flow regimes, a possibility that is less explored. Daniel Harris at Brown University, Rhode Island, and colleagues have studied the motion of a new system of self-propelled objects that move in this intermediate regime, finding that the objects organize into several distinct and tunable motion patterns [1]. The researchers say that their surfers may serve as a versatile, accessible model system for developing a detailed understanding of active matter in the intermediate flow regime.
The team considered millimeter-scale plastic “surfers” floating atop a vertically vibrated pool containing a mixture of water and glycerol. The surfers resembled miniature, rectangular boats and had uneven weight distributions across their lengths. With heavier sterns than bows, the surfers bobbed up and down like seesaws when the liquid surface vibrated. The waves that then emanated from the bow and stern of each surfer had unequal amplitudes, with the sterns creating waves with higher amplitudes.
Hussam Amrouch has developed an AI-ready architecture that is twice as powerful as comparable in-memory computing approaches. As reported in the journal Nature, the professor at the Technical University of Munich (TUM) applies a new computational paradigm using special circuits known as ferroelectric field effect transistors (FeFETs). Within a few years, this could prove useful for generative AI, deep learning algorithms, and robotic applications.
The basic idea is simple: unlike previous chips, where only calculations were carried out on transistors, they are now the location of data storage as well. That saves time and energy.
“As a result, the performance of the chips is also boosted,” says Hussam Amrouch, a professor of AI processor design at the Technical University of Munich (TUM).
