Autoimmune disorders such as ankylosing spondylitis are heavily linked to specific genetic human tissue types, particularly variations of the human leukocyte antigen B27. Traditional treatments for these debilitating conditions often rely on broad immunosuppression, which weakens a patient's entire immune defense and increases the risk of infections. To provide a more precise solution, UC Berkeley researchers have developed small-molecule ligands that selectively target and block a specific disease-associated variation of this allele, known as human leukocyte antigen B27:05. The therapeutic compounds feature a distinct three-part molecular architecture that includes a targeted binding group designed to fit securely into a specific molecular pocket, a flexible chemical linker, and a reactive group that forms a stable bond with a neighboring cysteine amino acid residue. By turning off only the specific genetic driver responsible for the autoimmune reaction, this technology opens the door to highly targeted therapies that treat the root cause of the disease while leaving the rest of the immune system fully functional.

The transition to 5G and 6G networks has led to a widespread adoption of machine learning (ML) for critical functions like modulation classification, channel estimation, resource management, and spectrum sensing. While ML has enhanced operational efficiency, it has simultaneously expanded the attack surface for adversarial ML at the Physical Layer (PHY), for example, from Generative Adversarial Networks (GANs). While techniques like radio frequency (RF) fingerprinting have emerged as a PHY-level authentication method based on hardware-induced signal traits (such as in-phase/quadrature (I/Q) imbalance and error vector magnitude), GANs can synthesize RF signals to mimic legitimate hardware-induced features up to 95% similarity. This is close enough to evade most detection schemes. Existing defenses to GANs based on convolutional neural networks, deep neural networks, supervised retraining, and/or heuristics do not generalize well across different modulations, protocols, channel conditions, or unseen attack types. Autoencoder and reconstruction-based approaches are often limited to clean reference signals, which are not always available in dynamic wireless environments. While GANs are excellent at mimicking low-order statistics (mean/variance), they fail to replicate complex signal structures.

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A breakthrough in synthetic biology: an evolved DNA polymerase that synthesizes natural and modified RNA, paving the way for advancements in epigenetics, vaccine development, and drug discovery.