A team of researchers led by Rice University, in collaboration with colleagues in Mozambique and The University of Texas MD Anderson Cancer Center, has developed a simple, affordable human papillomavirus (HPV) test that delivers results in less than an hour with no specialized laboratory required. The breakthrough could provide an option for women in low-resource settings to be screened and treated for cervical cancer in a single clinic visit, a step that global health experts say could save countless lives. The research was recently published in Nature Communications.

ELSA – European Lighthouse on Secure and Safe AI is a European Network of Excellence (NoE) whose 26 members have been conducting outstanding research in the fields of Secure and Safe AI, Technical Robustness and Safety, Privacy-Preserving Techniques and Infrastructure, and Human Agency and Oversight. In June 2025, the network began its expansion. ELSA now welcomes fifteen more members: universities, research institutions, and ELLIS Units. With these, ELSA enhances its expertise in pressing AI research topics and fosters knowledge exchange.

A team of scientists from TTUHSC’s Jerry H. Hodge School of Pharmacy and Graduate School of Biomedical Sciences has published new evidence suggesting that the blood-brain barrier (BBB) remains largely intact in a commonly used mouse model of Alzheimer’s disease. The discovery challenges long-standing assumptions that Alzheimer’s disease causes the BBB to “leak,” potentially reshaping how researchers think about drug delivery for the disease. Fluids and Barriers of the CNS published the study July 23.

Accurate positioning is vital for applications ranging from autonomous vehicles to precision agriculture, yet atmospheric disturbances often compromise Global Navigation Satellite System (GNSS) performance.

A new framework for generative diffusion models was developed by researchers at Science Tokyo, significantly improving generative AI models. The method reinterpreted Schrödinger bridge models as variational autoencoders with infinitely many latent variables, reducing computational costs and preventing overfitting. By appropriately interrupting the training of the encoder, this approach enabled development of more efficient generative AI, with broad applicability beyond standard diffusion models.

A new class of highly efficient and scalable quantum low-density parity-check error correction codes, capable of performance approaching the theoretical hashing bound, has been developed by scientists at Institute of Science Tokyo, Japan. These novel error-correction codes can handle quantum codes with hundreds of thousands of qubits, potentially enabling large-scale fault-tolerant quantum computing, with applications in diverse fields, including quantum chemistry and optimization problems.