Results 161 to 170 of about 23,718 (313)

Modelling syntactic development in a cross-linguistic context [PDF]

, 2004
Mainstream linguistic theory has traditionally assumed that children come into the world with rich innate knowledge about language and grammar. More recently, computational work using distributional algorithms has shown that the information contained in ...
Freudenthal, D, Gobet, F, Pine, J M
core  

Ecological Adaptation Mechanisms Underlying Successful Plant Reproduction

Advanced Science, EarlyView.
During floral induction, various environmental and endogenous signals converge to regulate the florigen protein, which is transported from leaves to the SAM to initiate flowering. Within the SAM, a complex network of receptor kinases and small peptides orchestrates floral development with high spatiotemporal precision.
Hang Zhao, Shenghui Huo, Yue Xing, Shuxin Zhang, Guilin Li, Yihui Liu, Chongyang Jin, Fuguang Li, Xiaoyang Ge   +8 more
wiley   +1 more source

Integrating Spatial Proteogenomics in Cancer Research

Advanced Science, EarlyView.
Xx xx. ABSTRACT Background: Spatial proteogenomics marks a paradigm shift in oncology by integrating molecular analysis with spatial information from both spatial proteomics and other data modalities (e.g., spatial transcriptomics), thereby unveiling tumor heterogeneity and dynamic changes in the microenvironment.
Yida Wang, Yang Wu, Feng Zhang, Parthiban Periasamy, Haiyue You, Denise Goh, Rachel Elizabeth Ann Fincham, Xin Ning, Danping Wu, Lu Liu, Ying Jiang, Zhiwen Qian, Joe Yeong, Yan Zhang   +13 more
wiley   +1 more source

Generation of CCR4/CD7 Bispecific CAR‐T Cells Resistant to Fratricide and Exhaustion

Advanced Science, EarlyView.
The applications of CAR T‐cell therapy in T‐cell malignancies face limitations such as fratricide, effector‐cell exhaustion, and antigen‐escape. Herein, we developed fratricide‐ and exhaustion‐resistant CAR‐T cells that targeted CCR4 and CD7 simultaneously, with optional EGFRt safety switch. Additionally, scRNA‐seq unveiled new molecular targets, which
Sile Li, Yuanxin Li, Asif Rashid, Hong Kee Tan, Shing Chan, Man Yan Hui, Wilson Yau Ki Chan, Kee See Lam, Yinping Liu, Wenwei Tu, Wing Leung   +10 more
wiley   +1 more source

An Integrated NLP‐ML Framework for Property Prediction and Design of Steels

Advanced Science, EarlyView.
This study presents a data‐driven framework that uses language‐processing techniques to interpret steel processing descriptions and machine‐learning models to predict mechanical properties. By organising complex process histories into meaningful groups and enabling rapid property forecasts, the work supports faster, more informed steel design through ...
Kiran Devraju, Adithya Umanath Rai, Jose Thomson, Marzie Ghorbani, Shujing Zhao, Nick Birbilis   +5 more
wiley   +1 more source

Atomic Defects in Layered Transition Metal Dichalcogenides for Sustainable Energy Storage and the Intelligent Trends in Data Analytics

Advanced Science, EarlyView.
This review comprehensively summarizes the atomic defects in TMDs for their applications in sustainable energy storage devices, along with the latest progress in ML methodologies for high‐throughput TEM data analysis, offering insights on how ML‐empowered microscopy facilitates bridging structure–property correlation and inspires knowledge for precise ...
Zheng Luo, Ying Yang, Zizhen Fu, Fengqi Liu, Susu Fang, Kele Xu, Shanshan Wang   +6 more
wiley   +1 more source

Random Time‐Space Coding Metasurfaces for Spatial Control of the Temporal Statistics of Electromagnetic Fields

Advanced Science, EarlyView.
A probabilistic framework based on random time‐space coding metasurfaces enables control of the spatial distribution of electromagnetic fields temporal statistics. By tailoring the marginal and joint distributions of random codes, electromagnetic fields with desired mean and variance patterns are realized, enabling simultaneous transmission and jamming.
Jia Cheng Li, Jiang Han Bao, Che Liu, Tie Jun Cui   +3 more
wiley   +1 more source

Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy

Advanced Science, EarlyView.
Cancer immunotherapy faces challenges in predicting treatment responses and understanding resistance mechanisms. Artificial intelligence (AI) and machine learning (ML) offer powerful solutions for cancer immunotherapy in patient stratification, biomarker discovery, treatment strategy optimization, and foundation model development.
Xinchao Wu, Mengtao Sun, Lusheng Li, Jieqiong Wang, Shibiao Wan   +4 more
wiley   +1 more source

Diffusion–Model–Driven Discovery of Ferroelectrics for Photocurrent Applications

Advanced Science, EarlyView.
We developed a diffusion model–based generative AI and high‐throughput screening framework that accelerates the discovery of photovoltaic ferroelectrics. By coupling AI driven crystal generation with machine learning and DFT screening, we identified Ca3P2 and LiCdP as new ferroelectric materials exhibiting strong polarization, feasible switching ...
Byung Chul Yeo, Hyun‐Jae Lee, Sungwoo Kang, Jung‐Hoon Lee   +3 more
wiley   +1 more source

Physics‐Embedded Neural Network: A Novel Approach to Design Polymeric Materials

Advanced Science, EarlyView.
Traditional black‐box models for polymer mechanics rely solely on data and lack physical interpretability. This work presents a physics‐embedded neural network (PENN) that integrates constitutive equations into machine learning. The approach ensures reliable stress predictions, provides interpretable parameters, and enables performance‐driven, inverse ...
Siqi Zhan, Hengheng Zhao, Haotian Wang, Zhanjie Liu, Qian Li, Weifeng Zhang, Qingsong Zhao, Liqun Zhang, Jun Liu   +8 more
wiley   +1 more source