Results 241 to 250 of about 729,911 (289)

Macrophage Phenotype Detection Methodology on Textured Surfaces via Nuclear Morphology Using Machine Learning

Advanced Intelligent Discovery, EarlyView.
A novel machine learning approach classifies macrophage phenotypes with up to 98% accuracy using only nuclear morphology from DAPI‐stained images. Bypassing traditional surface markers, the method proves robust even on complex textured biomaterial surfaces. It offers a simpler, faster alternative for studying macrophage behavior in various experimental
Oleh Mezhenskyi, Kateryna Andriienkova, Tetyana Goldanova, Ezgi Antmen, Thibaut Soubrie, Nihal Engin Vrana   +5 more
wiley   +1 more source

Impact factors for quantifying country-level terrestrial biodiversity intactness footprints (IBIF). [PDF]

Sci Data
Schipper AM, van der Marel M, Bakkenes M, Giesen P, van Oorschot MMP, Wilting HC, Huijbregts MAJ, Marques A.   +7 more
europepmc   +1 more source

Artificial Intelligence‐Driven Insights into Electrospinning: Machine Learning Models to Predict Cotton‐Wool‐Like Structure of Electrospun Fibers

Advanced Intelligent Discovery, EarlyView.
Electrospinning allows the fabrication of fibrous 3D cotton‐wool‐like scaffolds for tissue engineering. Optimizing this process traditionally relies on trial‐and‐error approaches, and artificial intelligence (AI)‐based tools can support it, with the prediction of fiber properties. This work uses machine learning to classify and predict the structure of
Paolo D’Elia, Giovanna Pappalardo, Aldo R. Boccaccini, Liliana Liverani   +3 more
wiley   +1 more source

When did we start calling it silicosis? A historical perspective on the naming of an occupational disease. [PDF]

J Occup Med Toxicol
Zanatta A, Zampieri F, Magno G, Bollini S.   +3 more
europepmc   +1 more source

Accelerating Surface Composition Characterization of Thin‐Film Materials Libraries Using Multi‐Output Gaussian Process Regression

Advanced Intelligent Discovery, EarlyView.
To integrate surface analysis into materials discovery workflows, Gaussian process regression is used to accurately predict surface compositions from rapidly acquired volume composition data (obtained by energy‐dispersive X‐ray spectroscopy), drastically reducing the number of required surface measurements on thin‐film materials libraries.
Felix Thelen, Florian Lourens, Alfred Ludwig   +2 more
wiley   +1 more source

Spatial analysis of pollution source proximity to early childhood development centers in Gauteng. [PDF]

Environ Geochem Health
Mkunyana YP, Kootbodien T, Street R.
europepmc   +1 more source

Uranium mining and lung cancer: a legacy of the nuclear age. [PDF]

Carcinogenesis
Samet JM, Richardson DB.
europepmc   +1 more source

Machine learning-enhanced monitoring of global copper mining areas. [PDF]

Sci Data
Li H, Wang P, Werner TT, Chen B, Chen WQ.   +4 more
europepmc   +1 more source

Optimizing Detection Reliability in Safety-Critical Computer Vision: Transfer Learning and Hyperparameter Tuning with Multi-Task Learning. [PDF]

Sensors (Basel)
Broderick W, McConnell S.
europepmc   +1 more source

Research on surrounding rock control technology of roof cutting and pressure relieving for roadside filling in gob-side entry retaining of large mining height panel. [PDF]

Sci Rep
Weiyong L, Shengjun L, Yaohui S, Tao G, Wansheng M, Xiaowu Z.   +5 more
europepmc   +1 more source