Results 71 to 80 of about 9,958 (247)
Stem cell differentiation follows a conserved surface condensate trajectory: H3K27ac super enhancers nucleate large RNA polymerase II clusters that grow and unfold before transcriptional activity disperses them. This work reveals how biophysical forces at enhancer surfaces dynamically build and dismantle stem cell transcription hubs, reshaping cell ...
Tim Klingberg +18 more
wiley +1 more source
A brain‐targeted nanoparticle enables delivery of a therapeutic nanobody (Nb.29E9) that inhibits pathogenic GSK3β signaling. This intervention restores AMPK/mTORC1/TGFβ homeostasis, attenuates neuroinflammation and oxidative stress, and promotes long‐term functional recovery after ischemic stroke.
Lan Li +14 more
wiley +1 more source
Chinese Grammatical Error Correction: A Survey
Chinese Grammatical Error Correction (CGEC) is a critical task in Natural Language Processing, addressing the growing demand for automated writing assistance in both second-language (L2) and native (L1) Chinese writing. While L2 learners struggle with mastering complex grammatical structures, L1 users also benefit from CGEC in academic, professional ...
Mengyang Qiu +6 more
openaire +2 more sources
Large Language Model‐Based Chatbots in Higher Education
The use of large language models (LLMs) in higher education can facilitate personalized learning experiences, advance asynchronized learning, and support instructors, students, and researchers across diverse fields. The development of regulations and guidelines that address ethical and legal issues is essential to ensure safe and responsible adaptation
Defne Yigci +4 more
wiley +1 more source
Four decades of retinal vessel segmentation research (1982–2025) are synthesized, spanning classical image processing, machine learning, and deep learning paradigms. A meta‐analysis of 428 studies establishes a unified taxonomy and highlights performance trends, generalization capabilities, and clinical relevance.
Avinash Bansal +6 more
wiley +1 more source
Automatical sampling with heterogeneous corpora for grammatical error correction
Thanks to the strong representation capability of the pre-trained language models, supervised grammatical error correction has achieved promising performance.
Shichang Zhu +3 more
doaj +1 more source
Grammatical Error Correction with Neural Reinforcement Learning
We propose a neural encoder-decoder model with reinforcement learning (NRL) for grammatical error correction (GEC). Unlike conventional maximum likelihood estimation (MLE), the model directly optimizes towards an objective that considers a sentence-level, task-specific evaluation metric, avoiding the exposure bias issue in MLE.
Keisuke Sakaguchi +2 more
openaire +3 more sources
Text correction systems (e.g., spell checkers) have been used to improve the quality of computerized text by detecting and correcting errors. However, the task of performing spelling correction and word normalization (text correction) for Thai social ...
Anuruth Lertpiya +2 more
doaj +1 more source
3D‐Printed Shark‐Inspired Soft–Hard Hybrid Underwater Robot With Buoyancy Control and Onboard Vision
A fully self‐contained shark‐inspired underwater robot is developed using 3D‐printed soft–hard hybrid structures, servo‐driven propulsion, and pump‐based buoyancy control. The platform achieves three‐dimensional locomotion and onboard vision‐based target tracking, offering a reproducible and accessible framework for biomimetic underwater robot research.
Shotaro Saito +3 more
wiley +1 more source
Evaluation of Really Good Grammatical Error Correction
Although rarely stated, in practice, Grammatical Error Correction (GEC) encompasses various models with distinct objectives, ranging from grammatical error detection to improving fluency. Traditional evaluation methods fail to fully capture the full range of system capabilities and objectives.
Östling, Robert +4 more
openaire +4 more sources

