Results 111 to 120 of about 211,846 (266)

Data‐Driven Printability Modeling of Hydrogels for Precise Direct Ink Writing Based on Rheological Properties

open access: yesAdvanced Science, EarlyView.
A predictive model for 3D printability is developed by integrating rheological analysis, including the Large Amplitude Oscillatory Shear (LAOS) test, with machine learning. With prediction errors under 10%, the model shows that post‐extrusion recovery controls horizontal printability, while high‐strain‐rate nozzle flow dictates vertical printability ...
Eun Hui Jeong   +7 more
wiley   +1 more source

The Safety Risks of AI-Driven Solutions in Autonomous Road Vehicles

open access: yesWorld Electric Vehicle Journal
At present Deep Neural Networks (DNN) have a dominant role in the AI-driven Autonomous driving approaches. This paper focuses on the potential safety risks of deploying DNN classifiers in Advanced Driver Assistance System (ADAS) systems.
Farshad Mirzarazi   +2 more
doaj   +1 more source

CellPolaris: Transfer Learning for Gene Regulatory Network Construction to Guide Cell State Transitions

open access: yesAdvanced Science, EarlyView.
CellPolaris decodes how transcription factors guide cell fate by building gene regulatory networks from transcriptomic data using transfer learning. It generates tissue‐ and cell‐type‐specific networks, identifies master regulators in cell state transitions, and simulates TF perturbations in developmental processes.
Guihai Feng   +27 more
wiley   +1 more source

Immune Predictors of Radiotherapy Outcomes in Cervical Cancer

open access: yesAdvanced Science, EarlyView.
This study reveals dynamic immune remodeling in cervical cancer following radiotherapy. Single‐cell analysis identifies the C3/C3AR1 axis as a central mediator of epithelial–myeloid crosstalk, whose inhibition reduces treatment efficacy in mice. Guided by these insights, the eight‐feature machine‐learning model: Cervical Cancer Radiotherapy Immune ...
Linghao Wang   +8 more
wiley   +1 more source

Comprehensive Profiling of N6‐methyladnosine (m6A) Readouts Reveals Novel m6A Readers That Regulate Human Embryonic Stem Cell Differentiation

open access: yesAdvanced Science, EarlyView.
This research deciphers the m6A transcriptome by profiling its sites and functional readout effects: from mRNA stability, translation to alternative splicing, across five different cell types. Machine learning model identifies novel m6A‐binding proteins DDX6 and FXR2 and novel m6A reader proteins FUBP3 and L1TD1.
Zhou Huang   +11 more
wiley   +1 more source

Potent and Long‐Lasting Immunogenicity Generated by LNP‐mRNA gE Antigen Against Varicella Zoster Virus via an AI‐Assisted Pipeline

open access: yesAdvanced Science, EarlyView.
This study designs a novel mRNA‐LNP vaccine targeting VZV glycoprotein E (gE) for herpes zoster via an AI‐assisted pipeline. Validated in mice and rhesus macaques, the mRNA‐LNP vaccine shows strong humoral and cellular immune responses, with CD4+ T‐cell responses more effective and durable than Shingrix, offering a promising prophylactic option ...
Kai Dong   +6 more
wiley   +1 more source

Approach for Tattoo Detection and Identification Based on YOLOv5 and Similarity Distance

open access: yesApplied Sciences
The large number of images in the different areas and the possibilities of technologies lead to various solutions in automatization using image data. In this paper, tattoo detection and identification were analyzed.
Gabija Pocevičė   +3 more
doaj   +1 more source

Collaborative hyperparameter tuning

open access: yes, 2013
Hyperparameter learning has traditionally been a manual task because of the limited number of trials. Today's computing infrastructures allow bigger evaluation budgets, thus opening the way for algorithmic approaches. Recently, surrogate-based optimization was successfully applied to hyperparameter learning for deep belief networks and to WEKA ...
Bardenet, R.   +3 more
openaire   +1 more source

Unpaired Learning‐Enabled Nanotube Identification from AFM Images

open access: yesAdvanced Science, EarlyView.
Identifying nanotubes on rough substrates is notoriously challenging for conventional image analysis. This work presents an unpaired deep learning approach that automatically extracts nanotube networks from atomic force microscopy images, even on complex polymeric surfaces used in roll‐to‐roll printing.
Soyoung Na   +10 more
wiley   +1 more source

Diffusion‐MRI‐Based Estimation of Cortical Architecture via Machine Learning (DECAM) in Primate Brains

open access: yesAdvanced Science, EarlyView.
We present Diffusion‐MRI‐based Estimation of Cortical Architecture via Machine Learning (DECAM), a deep‐learning framework for estimating primate brain cortical architecture optimized with best response constraint and cortical label vectors. Trained using macaque brain high‐resolution multi‐shell dMRI and histology data, DECAM generates high‐fidelity ...
Tianjia Zhu   +7 more
wiley   +1 more source

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