Researchers at KAUST and Peking University Third Hospital have created a novel blastoid model for studying early human development using extended pluripotent stem cells (EPSCs). The blastoid is a 3D cell model mimicking the blastocyst phase, avoiding ethical concerns associated with using human embryos. The team showed that blastoids can be cultured to mimic post-implantation development, offering insights into early cell lineages. Why it matters: This innovation provides a way to study human embryogenesis without the ethical constraints of using actual embryos, potentially advancing our understanding of miscarriage and birth defects.
KAUST researchers have developed deepBlastoid, a deep learning tool for evaluating models of human embryo development, called blastoids. deepBlastoid can evaluate images of blastoids at speeds 1000 times faster than expert scientists, processing 273 images per second. Trained on over 2000 microscopic blastoid images, it assesses the impact of chemicals on blastoid development using over 10,000 images. Why it matters: This AI tool accelerates research into early pregnancy, fertility complications, and the impact of chemicals on embryo development, with implications for reproductive technologies.
KAUST's Laboratory of Stem Cells and Diseases, led by Assistant Professor Antonio Adamo, uses induced pluripotent stem cells (iPSCs) to model diseases like diabetes. The lab employs a reprogramming technique to revert patient fibroblasts into iPSCs, enabling the study of disease progression in vitro. Adamo's research focuses on enzymes and disregulated transcriptional/epigenetic mechanisms to understand disease onset. Why it matters: This research contributes to regenerative medicine and offers insights into metabolic diseases relevant to the GCC region.
KAUST researchers discovered a five-hectare bio-sedimentary formation of living stromatolites off Sheybarah Island in the Red Sea. These structures are microbial carbonates similar to fossils of early life and are only the second group found in normal marine settings. The stromatolites host a diverse microbial community, including reticulated filaments previously only found in caves. Why it matters: The discovery provides insights into early life on Earth and has implications for understanding potential life formation on Mars, while also creating a unique educational opportunity for tourism in Saudi Arabia.
KAUST researchers demonstrated a new flash memory device design using gallium oxide, which can withstand harsh environments. In collaboration with the University of Michigan, KAUST researchers explained a key molecular event for the activation of an enzyme associated with cancer. The Summer 2023 issue of KAUST Discovery is now available. Why it matters: These research achievements highlight KAUST's contributions to advanced materials science and biomedical research, with potential applications in space technology and cancer treatment.
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.
KAUST Professor Carlos Duarte will receive the Blaise Pascal Medal from the European Academy of Sciences in October 2018 for his contributions to Earth and environmental sciences. Duarte is the Tarek Ahmed Juffali research chair in Red Sea ecology. He is involved in KAUST's Red Sea Research Center and supports Saudi Vision 2030 initiatives like The Red Sea Project and NEOM. Why it matters: The award recognizes KAUST's impact in environmental science and Duarte's role in aligning research with Saudi Arabia's development goals.
Researchers at the Rosalind Franklin Institute are using generative AI, including GANs, to augment limited biological datasets, specifically mirtron data from mirtronDB. The synthetic data created mimics real-world samples, facilitating more comprehensive training of machine learning models, leading to improved mirtron identification tools. They also plan to apply Large Language Models (LLMs) to predict unknown patterns in sequence and structure biology problems. Why it matters: This research explores AI techniques to tackle data scarcity in biological research, potentially accelerating discoveries in noncoding RNA and transposable elements.