UW-Madison researchers have developed a non-viral method of developing CAR NK cells. The inventors tested many

UW-Madison researchers have developed a non-viral method of developing CAR NK cells. The inventors tested many parameters along established processes for genetically modifying immune cells without virus. They found that using feeder cells to support the growth and proliferation of the modified NK cells was important. They also learned that the timing and pulse used for electroporation of the cells affected gene editing efficiency. Finally, using two different small molecules during and after the transfection step positively impacted gene editing efficiency. Two previously reported methods of non-viral gene knock-in methods for modifying NK cells resulted in 7.5% gene editing efficiency and 16% gene editing efficiency in a modified method. The UW researchers report gene editing efficiency >20% using their method.

The inventors knocked out the NKG2A on the NK cells and added a CAR that recognizes GD2 receptor on tumor cells (the target of CAR T cells). NKG2A inhibits the activity of NK cells when the tumor produces an HLA-E ligand that binds that NK cell receptor. The removal of this receptor and addition of the CAR happens through a single CRISPR/Cas event. Other CAR NK cells have been manipulated like this but have used two genetic events which is time consuming and weakens the cells.

The Manifold-Constrained Model Compression (MCNC) software introduces an approach to compress foundational AI models like

The Manifold-Constrained Model Compression (MCNC) software introduces an approach to compress foundational AI models like Vision Transformers and large language models such as GPT and LLaMA. Unlike traditional methods, MCNC uses manifold-constrained optimization to achieve over 100 times compression, enhancing storage, communication, and customization efficiency without compromising model performance. less

UW-Madison researchers have developed a novel method for performing highly efficient trans-kingdom genetic conjugation from bacteria

UW-Madison researchers have developed a novel method for performing highly efficient trans-kingdom genetic conjugation from bacteria to fungi. They further developed the method into a system to kill specific fungi using a non-repair CRISPR-Cas system. The trans-kingdom conjugation system utilizes a plasmid that contains selection markers for bacteria and for yeast as well as the oriT sequence for conjugative transfer of the plasmid into recipients. The researchers found that they could control conjugation by tuning the populations of the donor and recipient. Since media used in the co-culturing of the organisms is resource constrained, and bacteria are more efficient at using the resources than fungi; the key to getting high efficiency conjugation is to keep the growth rate of the bacteria relatively low as compared to the growth rate of the fungi. The non-repair CRIPSR-Cas system can be transferred from bacteria into fungi. The researchers targeted a fungal gene essential for survival with the single guide RNA sequences and upon co-culturing with the donor bacteria, found recipient fungi died relatively quickly.

The inventors believe that this trans-kingdom conjugation system will be broadly applicable across fungal species although they have only tested it with E. coli and S. cerevisiae. They have done extensive modeling to explore the effects of media conditions on the donor and recipient populations since the ratio of the populations proved critical to getting high efficiency conjugation. They also explored the mechanism of conjugation and found mannose receptors are critical to the conjugation between the bacteria and yeast (adding mannose to the media to inhibit these receptors disrupted conjugation). Mannoproteins are ubiquitous among fungal cells, so the method should work for any fungal cell.

Deep Harmonic Finesse (DHF) is a method designed to address the challenges of separating quasi-periodic, non-stationary

Deep Harmonic Finesse (DHF) is a method designed to address the challenges of separating quasi-periodic, non-stationary signals from a single mixed signal input. This technology is pivotal for wearable systems where collecting large datasets is impractical. By leveraging deep harmonic neural networks and a pattern alignment method, DHF can isolate target signals from noise and other quasi-periodic phenomena, leveraging prior knowledge of time-frequency patterns. This approach is particularly beneficial in applications such as tissue oximetry and blood glucose monitoring, where accurate signal separation can significantly enhance the reliability of sensor readings. less

The technology comprises innovative liquid electrolyte solutions for redox flow batteries (RFBs), featuring a mix of

The technology comprises innovative liquid electrolyte solutions for redox flow batteries (RFBs), featuring a mix of specifically chosen cations and anions that improve conductivity, reduce overpotential, and enhance the overall energy efficiency and cycling stability of RFBs. This breakthrough addresses the critical challenges of low energy efficiency and cycling stability in non-aqueous redox flow batteries. less