Results 191 to 200 of about 15,054,030 (290)

Learning reorganizes dendritic and stabilizes axon initial segment inhibitory synapses in CA1 pyramidal neurons. [PDF]

Nat Commun
Klimmt H, Kuhn F, Kappel D, Murthy B, Ulivi AF, Argunşah AÖ, Vieweg S, Huettl RE, Remy S, Attardo A.   +9 more
europepmc   +1 more source

LLM‐Based Multimodal Robotic Endoscope Control Framework for Enhancing Human–Robot Interaction in Minimally Invasive Surgery

Advanced Intelligent Systems, EarlyView.
An large language model‐powered multimodal framework is developed for robotic endoscope control. It integrates speech recognition and real‐time instrument tracking, achieving 89.47% command accuracy with ~1s latency for natural human–robot interaction in minimally invasive surgery.
Yisen Huang, Wing Yin Ng, Qi Dou, Xixin Wu, Shannon Melissa Chan, Yunhui Liu, Philip Wai Yan Chiu, Zheng Li   +7 more
wiley   +1 more source

Prediction of Clinically Meaningful Improvement After Internet-Delivered Cognitive Behavioral Therapy for Depression and Anxiety Disorders: Machine Learning-Based Predictive Model Development and Temporal Validation Study. [PDF]

J Med Internet Res
Kravchenko O, Halvorsen M, Bäckman J, Kaldo V, Crowley JJ, Kuja-Halkola R, Rück C, Wallert J.   +7 more
europepmc   +1 more source

Development of an Inchworm‐Like Dual‐Balloon‐Assisted Colonoscopic Robot Based on a Two‐Segment Extensible Continuum Section

Advanced Intelligent Systems, EarlyView.
This work presents an inchworm‐like dual‐balloon‐assisted colonoscopic robot (IDCR) based on a rod‐driven two‐segment continuum section. The IDCR enables the active straightening of the sigmoid colon through an inchworm‐like locomotion strategy, thereby avoiding loop formation.
Dezhi Song, Bohan Leng, Yi Liu, Xiangyang Yu, Chaoyang Shi   +4 more
wiley   +1 more source

Explainable Machine Learning for Predicting Complicated Appendicitis and Expected Hospital Length of Stay in Children: An Exploratory Single-Center Study. [PDF]

Medicina (Kaunas)
Turki A, Raml E, Aboulola JE.
europepmc   +1 more source

Revealing Hidden Raman Signatures Through Attention‐Based Spectral Unmixing

Advanced Intelligent Systems, EarlyView.
Weak Raman signatures are recovered from background‐dominated spectra using a transformer‐based, reference‐free spectral unmixing AI framework. Self‐attention reconstructs substrate contribution directly from mixed data, enabling reliable extraction of previously inaccessible vibrational features.
Dmitriy A. Poteryayev, Pietro Novelli, Annalisa Coriolano, Riccardo Dettori, Valentina Tozzini, Fabio Beltram, Massimiliano Pontil, Antonio Rossi, Stiven Forti, Camilla Coletti   +9 more
wiley   +1 more source

Simultaneous Preoperative Prediction of Locally Advanced Breast Cancer, DCIS Component, and Multifocality Using Structured Mammographic Features and Gradient-Boosting Machine Learning. [PDF]

Diagnostics (Basel)
Raeiskarimi S, Saeedi-Moghadam M, Zarei F, Zeinali-Rafsanjani B.   +3 more
europepmc   +1 more source

Integration of Liquid Handling Robotics, Turbidimetry, and Machine Learning Toward Inverse Design of Lower Critical Solution Temperature Polymer Formulations

Advanced Intelligent Systems, EarlyView.
An integrated OT‐2/plate reader platform automates LCST cloud point measurement of NIPAM‐co‐DMAEMA copolymers across high‐dimensional formulation spaces. Machine learning trained on the resulting high‐throughput datasets achieves R2 up to 0.96 and enables inverse design of polymer formulations to target transition temperatures with <1% error.
Lachlan Alexander, Vianna F. Jafari, Tanja Junkers   +2 more
wiley   +1 more source

Automatic uncertainty evaluation for determining the number of components in nested models and the shrinkage regularization parameters. [PDF]

PLoS One
Martino L, San Millán-Castillo R, Morgado E.   +2 more
europepmc   +1 more source

How does the Kids SIPsmartER program impact the sugar‐sweetened beverage intake of students: An investigation beyond total treatment effect in randomized controlled trial

American Journal of Agricultural Economics, EarlyView.
Abstract This study develops and empirically estimates a structural framework to decompose the causal pathways of multilevel behavioral interventions targeting adolescent health behaviors. We apply this framework to the Kids SIPsmartER (KSS) program, a 6‐month, school‐based intervention evaluated through a clustered randomized controlled trial in rural
Naveen Abedin, Wen You, Annie Reid, Kathleen Porter, Brittany Kirkpatrick, Jamie Zoellner   +5 more
wiley   +1 more source