Organoid research has rapidly advanced as a transformative platform for modeling development, disease, and regeneration, yet inconsistent reporting has hindered reproducibility and limited data integration across laboratories. The newly introduced Minimum Information about Organoid Research (MIOR) framework establishes a comprehensive, modular reporting system designed to address these challenges. MIOR defines clear requirements for project metadata, biological sources, organoid characterization, culture conditions, engineering strategies, and assay parameters. By distinguishing essential from recommended fields, the framework balances rigor with practical usability. MIOR aims to turn organoid datasets into reusable, comparable resources and strengthen the reliability and translational potential of organoid-based research.

Simultaneous localization and mapping (SLAM) is widely used in autonomous driving, augmented reality, and embodied intelligence. In real-world settings, sensor measurements often suffer from substantial clutter (false alarms) and missed detections, which complicate SLAM data association. This complexity manifests as uncertainty in associating observations to landmarks, the possibility of erroneous associations between clutter and landmarks, and the potential absence of landmark observations. Random Finite Set (RFS) theory offers a Bayesian estimation framework well suited to SLAM with uncertain data association and an unknown, time-varying number of landmarks, and has spurred extensive research on RFS-based SLAM methods. Particle-filter-based Probability Hypothesis Density (PHD)-SLAM can effectively estimate the joint probability density of the pose and the map under clutter and missed detections, yielding robust SLAM performance. However, improving the estimation accuracy of particle-filter PHD-SLAM typically requires increasing the number of particles, which rapidly scales the computational cost.

SARS-CoV-2 evolves rapidly, creating challenges for traditional broad antibody development strategies that rely on conserved epitopes. By surveying 7,116 published receptor-binding domain（RBD）-targeting monoclonal antibodies, we identify three single monoclonal antibodies (mAbs)—SA55, VIR-7229, and BD55-1205—and one broadly neutralizing antibodies (bsAb) Dia-19, that retain ng (in the ng/mL range) neutralization activity even when their binding footprints overlap RBD residues with mutation rates up to 39%. Notably, the three mAbs above carry ~2× more VH somatic hypermutations than the dataset median. Guided by these observations, we outline two complementary strategies: (1) an immune trajectory strategy that prioritizes higher-maturity candidates, and (2) a viral fitness-constraint strategy suited to upgrading lower-maturity antibodies. Together, these provide practical paths for discovering and improving antibodies against fast-evolving SARS-CoV-2.

Developing high-efficiency sintering technologies with mild conditions is crucial for energy reducing and performances manipulating. However, sintering ceramics at low temperatures in short times without pressure is challenging. Inspired by microwave resonance and dissolution-precipitation phenomena, microwave cold sintering process (MW-CSP) is proposed here to densify high-performance ceramics with significantly reduced sintering times and temperatures under pressureless conditions. A range of ceramics including chlorides, oxides, phosphates and molybdates with various applications are shown to be well sintered by MW-CSP. The mechanical and dielectric properties of the selected materials are improved by 50-95%, while the energy consumption of MW-CSP is dramatically reduced by over 97% compared to other pressureless sintering technologies.

Aluminum Oxynitride (AlON) transparent ceramics are recognized as one of the most promising transparent ceramic materials in the 21st century, combining high optical transmittance with excellent mechanical properties. However, producing high-transmittance AlON ceramics via pressureless sintering (also known as conventional sintering, CS) has consistently faced the challenge of excessively long dwell durations at high temperatures (6-30 h). Prolonged sintering not only leads to high risks, high energy consumption, low efficiency, and elevated costs, but also results in excessive grain growth, degrading mechanical performance. In this study, based on the CS route and incorporating the emerging technique of ultra-fast high-temperature sintering (UHS), we propose a novel strategy-UHS combined with CS (UHS+CS)-for efficiently fabricating highly transparent AlON ceramics. This approach achieves remarkable technical outcomes, and the underlying mechanisms are clarified.

The proliferation of rooftop solar panels and distributed batteries in residential neighborhoods has created new challenges for power grid operators. Blockchain technology is emerging as a promising solution for enabling secure energy trading among these networked communities. However, designing a blockchain system that can handle the real-time operational requirements and cybersecurity concerns of actual power systems remains a critical challenge. To address this issue, researchers at Illinois Institute of Technology developed and tested a permissioned blockchain system on networked microgrids connecting the IllinoisTech campus with the Bronzeville community in Chicago, demonstrating significant cost savings and revenue increases for participating neighborhoods.

Researchers at Northwestern University have reviewed emerging strategies for recovering ammonia from wastewater using redox-active materials. These “redox reservoirs” enable selective, membrane-free ammonia capture powered by renewable electricity or even spontaneously via organic oxidation, paving the way toward a circular nitrogen economy.

Onboard model, capable of providing estimated measurable values and unmeasurable performance parameters of interest with the maximal fidelity, serves as the cornerstone for aircraft engine control and fault diagnosis. As aircraft engine configurations grow increasingly complex to meet the performance specifications of next-generation propulsion systems, significant challenges is proposed to the accuracy and real-time performance of onboard models. Consequently, the development of onboard modeling techniques has become increasingly crucial.

To answer this question: Can generative AI improve vehicle trajectory prediction in car-following scenarios? Researchers from the University of Wisconsin–Madison, Tongji University, and collaborators developed FollowGen, a conditional diffusion model that integrates historical motion features and inter-vehicle interactions to generate safer and more reliable trajectory predictions for autonomous driving.