A retrospective study of 17 drug-resistant epilepsy patients found that stereo-electroencephalography-guided radiofrequency thermocoagulation (RF-TC) rapidly altered brain network connectivity after treatment. Reductions in alpha-band connectivity within epileptogenic regions were linked to clinical outcomes, suggesting that short resting-state recordings may help predict treatment response. Findings support RF-TC as a network-modulating therapy rather than a purely local lesioning procedure and highlight its potential role in guiding more personalized epilepsy care.

Researchers have introduced a novel canonical correlation guided deep neural network (CCDNN) to improve multi-source data acquisition and fusion. The model is designed to integrate heterogeneous data more effectively, addressing limitations of existing fusion methods in handling diverse and high-dimensional inputs. By leveraging advanced neural network structures, CCDNN enhances feature representation and decision-making accuracy. Experimental results demonstrate superior performance across benchmark tasks, highlighting its potential for applications in intelligent control, automation, and data-driven engineering systems.

New research led by the University of Oxford and University College London (UCL) has revealed that pollution from coal-fired power plants is significantly reducing the energy output of solar photovoltaic (solar PV) installations, particularly where these are expanding side by side. The findings have been published today (15 May) in Nature Sustainability.

Researchers at Tampere University, Finland, have developed a groundbreaking 3D‑printed ceramic implant material that closely mimics real human bone. The findings advance the development of personalised bone regeneration and may lead to more effective and accessible treatments for bone defects.