KAUST researchers collaborated with the Blue Brain Project to study astrocytes, brain cells crucial for memory and learning. Dr. Corrado Calì produced 3D models of astrocytes using serial block-face electron microscopy to understand their structure. The study, published in Progress in Neurobiology, reveals how lactate transfer from astrocytes to neurons contributes to brain energy usage. Why it matters: Understanding astrocyte function could lead to new drugs for treating conditions like stroke and Alzheimer's disease by improving brain cell function.
Researchers from MBZUAI have developed XReal, a diffusion model for generating realistic chest X-ray images with precise control over anatomy and pathology location. The model utilizes an Anatomy Controller and a Pathology Controller to introduce spatial control in a pre-trained Text-to-Image Diffusion Model without fine-tuning. XReal outperforms existing X-ray diffusion models in realism, as evaluated by quantitative metrics and radiologists' ratings, and the code/weights are available.
KAUST Discovery Associate Professor Stefan Arold has established KAUST's first structural biology lab specializing in determining the atomic 3D structure of proteins and other biological macromolecules. The lab setup involved challenges such as assembling instruments and continuing research, but the Bioscience Core Lab at KAUST and support from colleagues aided in the process. Arold's research focuses on understanding protein function through an integrated 'hybrid' approach to analyze 3D structure and function of proteins. Why it matters: This new lab enhances KAUST's capabilities in molecular biophysics and structural biology, enabling advanced research into the functions of proteins and their implications for health and disease.
Egor Zakharov from ETH Zurich AIT lab will present research on creating controllable and detailed 3D head avatars using data from consumer-grade devices. The presentation will cover high-fidelity image-based facial reconstruction/animation and video-based reconstruction of detailed structures like hairstyles. He will showcase integrating human-centric assets into virtual environments for real-time telepresence and entertainment. Why it matters: This research contributes to advancements in digital human modeling and telepresence, with applications in communication and gaming within the region.
KAUST hosted the KAUST Research Conference: Advances in Well Construction with Focus on Near-Wellbore Physics and Chemistry from November 7 to 9. The conference was co-chaired by Eric van Oort, a professor at UT Austin, and Tadeusz Patzek, director of the University’s Upstream Petroleum Engineering Research Center. Attendees included professors from the University of Queensland and UT Austin, and directors from GenesisRTS and Labyrinth Consulting Services, Inc. Why it matters: The conference facilitates international collaboration on advancements in petroleum engineering and well construction technologies, which are strategically important for Saudi Arabia.
KAUST researchers used cryogenic electron microscopy (cryo-EM) to study the 3D structure of protein complexes involved in DNA replication and repair. They investigated the interaction between the Y-family TLS polymerase Pol K and mono-ubiquitylated PCNA. The study revealed that DNA binding is required for Pol K to form a rigid, active complex with PCNA. Why it matters: Understanding these structural interactions may provide insights into cancer development and drug resistance mechanisms.
Antonio Adamo is an assistant professor of bioscience in the Biological and Environmental Science and Engineering Division at KAUST. He is highlighted in a KAUST faculty focus. Why it matters: Showcases KAUST's faculty expertise in bioscience.
Pascal Fua from EPFL presented an approach to implementing convolutional neural nets that output complex 3D surface meshes. The method overcomes limitations in converting implicit representations to explicit surface representations. Applications include single view reconstruction, physically-driven shape optimization, and bio-medical image segmentation. Why it matters: This research advances geometric deep learning by enabling end-to-end trainable models for 3D surface mesh generation, with potential impact on various applications in computer vision and biomedical imaging in the region.