The microtubule organizing centers (MTOCs) of oocytes are essential for meiotic spindle assembly and for ensuring precise chromosome segregation. The detailed dynamic changes of MTOCs in germinal vesicle (GV) oocytes - a stage where early events of MTOC maturation happen - remain unclear. Recently, a study published in Science Bulletin explored the dynamics of MTOCs maturation in GV oocytes and disclosed the key factors involved in these processes. According to the investigation, MTOCs maturation is required for spindle assembly and may play an unrecognized role in oocyte aging.

The Korea Research Institute of Standards and Science (KRISS, President Lee Ho Seong) has developed a diagnostic platform that amplifies the unique optical signals of molecules by more than a hundred million times, enabling the precise detection and quantification of trace amounts of Alzheimer’s disease biomarkers in body fluids.

North American river otters eat, play and defecate in the same place. But their terrible food hygiene make them ideal for detecting future health threats in the environment, according to scientists. In a new study published Aug. 14, Smithsonian scientists analyzed the otters’ diets and “latrine” habitats in the Chesapeake Bay for the first time. They discovered river otters often eat food riddled with parasites—and that may not be a bad thing for the larger ecosystem.

The rise of large-scale artificial intelligence (AI) models, such as ChatGPT, DeepSeek, and autonomous vehicle systems, has significantly advanced the boundaries of AI, enabling highly complex tasks in natural language processing, image recognition, and real-time decision-making. However, these models demand immense computational power and are often centralized, relying on cloud-based architectures with inherent limitations in latency, privacy, and energy efficiency. To address these challenges and bring AI closer to real-world applications, such as wearable health monitoring, robotics, and immersive virtual environments, innovative hardware solutions are urgently needed. This work introduces a near-sensor edge computing (NSEC) system, built on a bilayer AlN/Si waveguide platform, to provide real-time, energy-efficient AI capabilities at the edge. Leveraging the electro-optic properties of AlN microring resonators for photonic feature extraction, coupled with Si-based thermo-optic Mach–Zehnder interferometers for neural network computations, the system represents a transformative approach to AI hardware design. Demonstrated through multimodal gesture and gait analysis, the NSEC system achieves high classification accuracies of 96.77% for gestures and 98.31% for gaits, ultra-low latency (< 10 ns), and minimal energy consumption (< 0.34 pJ). This groundbreaking system bridges the gap between AI models and real-world applications, enabling efficient, privacy-preserving AI solutions for healthcare, robotics, and next-generation human–machine interfaces, marking a pivotal advancement in edge computing and AI deployment.

Solid-state batteries are widely recognized as the next-generation energy storage devices with high specific energy, high safety, and high environmental adaptability. However, the research and development of solid-state batteries are resource-intensive and time-consuming due to their complex chemical environment, rendering performance prediction arduous and delaying large-scale industrialization. Artificial intelligence serves as an accelerator for solid-state battery development by enabling efficient material screening and performance prediction. This review will systematically examine how the latest progress in using machine learning (ML) algorithms can be used to mine extensive material databases and accelerate the discovery of high-performance cathode, anode, and electrolyte materials suitable for solid-state batteries. Furthermore, the use of ML technology to accurately estimate and predict key performance indicators in the solid-state battery management system will be discussed, among which are state of charge, state of health, remaining useful life, and battery capacity. Finally, we will summarize the main challenges encountered in the current research, such as data quality issues and poor code portability, and propose possible solutions and development paths. These will provide clear guidance for future research and technological reiteration.

65LAB awards US$1.5 million to Duke-NUS platform to advance antifibrotic drug discovery. Recipient Professor Enrico Petretto from Duke-NUS is spearheading groundbreaking research to develop new drugs for lung disease and uncover new therapeutic targets for kidney disease.

Many New Zealand GPs have taken up the use of AI scribes to transcribe patient notes during consultations despite ongoing challenges with their legal and ethical oversight, data security, patient consent, and the impact on the doctor-patient relationship, a study led by the University of Otago, Wellington – Ōtākou Whakaihu Waka, Pōneke has found.