Results 181 to 190 of about 82,148 (341)

Biological Resectability in Colorectal Liver Metastases: A Nomogram‐Based Continuous Risk Model to Define Borderline Disease

Annals of Gastroenterological Surgery, EarlyView.
KRAS mutation demonstrated a prognostic impact according to primary tumor sidedness, independent of tumor morphology, in patients undergoing hepatectomy for colorectal liver metastases. A nomogram integrating biological and clinicopathological factors was developed to provide continuous biological risk assessment.
Yuzo Umeda, Toshiharu Mitsuhashi, Tomokazu Fuji, Toru Kojima, Takeshi Koujima, Tatsuo Matsuda, Daisuke Satoh, Masaru Inagaki, Kosei Takagi, Toshiyoshi Fujiwara   +9 more
wiley   +1 more source

Evolution of Physical Intelligence Across Scales

Advanced Intelligent Discovery, EarlyView.
By following the evolution of physical intelligence across scales, this article shows how intelligence arises from materials, structures, physical interactions, and collectives. It establishes physical intelligence as the evolutionary foundation upon which embodied intelligence is built.
Ke Liu, Tianyun Huang, Aming Li, Pengyu Lv, Tingting Qin, Wenhui Chen, Hui Wei, Huiling Duan   +7 more
wiley   +1 more source

Hooves, Sensors, and Signals: Precision Approaches to Automated Lameness Detection in Dairy Cattle. [PDF]

Animals (Basel)
Hudson CC, Nicodemus MC, McGee MM, McKnight MG, Harvey KM.   +4 more
europepmc   +1 more source

A Multimodal Intelligent System for Human Digital Twin Simulation with Continuous Kinematic Data Tracking, Biometric Prognosis, and Cognitive State Feedback in Industrial Environments

Advanced Intelligent Discovery, EarlyView.
This article implements a unified human digital twin framework that integrates cutting edge actuation, sensing, simulation, and bidirectional feedback capability. The approach includes integrating multimodal sensing, AI, and biomechanical simulation into one compact system.
Tajbeed Ahmed Chowdhury, Sebastian Gratz‐Kelly, Eric Wagner, Paul Motzki, Martina Lehser   +4 more
wiley   +1 more source

From veterinary practice to science: the transformation of dairy cow lameness in Britain, c.1930-1987. [PDF]

Front Vet Sci
Woods A.
europepmc   +1 more source

An Autonomous Large Language Model‐Agent Framework for Transparent and Local Time Series Forecasting

Advanced Intelligent Discovery, EarlyView.
Architecture of the proposed large language model (LLM)‐based agent framework for autonomous time series forecasting in thermal power generation systems. The framework operates through a vertical pipeline initiated by natural language queries from users, which are processed by the LLM Agent Core powered by Llama.cpp and a ReAct loop with persistent ...
William Gouvêa Buratto, Gabriel Villarrubia Gonzalez, Ademir Nied, Laio Oriel Seman, Anne Carolina Rodrigues Klaar, Stefano Frizzo Stefenon   +5 more
wiley   +1 more source

Lesion-specific impacts of lameness on milk yield and composition in Holstein dairy cows. [PDF]

Vet Anim Sci
Khorrami B, Soltani Azar N, Assadi-Alamouti A, Bakhtiarizadeh MR, Pourhosseini E.   +4 more
europepmc   +1 more source

AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective

Advanced Intelligent Discovery, EarlyView.
The exponential growth of cancer multi‐omics data brings opportunities and challenges for precision oncology. This review systematically examines AI's role in addressing these challenges, covering generative models, integration architectures, Explainable AI for clinical trust, clinical applications, and key directions for clinical translation.
Shilong Liu, Shunxiang Li, Kun Qian
wiley   +1 more source

Further Evaluation of 1,25(OH)₂D₃-Glycosides Inclusion Timing for Reducing Clinical Bacterial Chondronecrosis with Osteomyelitis-Induced Lameness Incidence in Broilers. [PDF]

Animals (Basel)
Do ADT, Perera R, Kalanjati WW, Al-Mitib L, Zeman Z, Ahmed MR, Mummadi S, Castaño G, Agudelo J, Bühler K, Van der Klis JD, Kidd MT, Alrubaye A.   +12 more
europepmc   +1 more source

Interpretable Machine Learning for Bandgap Prediction and Descriptor‐Guided Design Rules of Phosphates

Advanced Intelligent Discovery, EarlyView.
An explainable CatBoost model was trained to predict the bandgaps of 474 phosphate crystals based on composition and density descriptors. SHAP analysis identified two key variables—d‐electron‐count dispersion and atomic‐density dispersion—as the primary drivers of the model's predictions.
Wenhu Wang, Abudukadi Tudi, Ran An, Zhihua Yang   +3 more
wiley   +1 more source