Results 171 to 180 of about 1,009,382 (298)

Identification of Proteases and Protease Inhibitors in Seeds of the Recalcitrant Forest Tree Species Quercus ilex. [PDF]

Front Plant Sci, 2022
Escandón M, Bigatton ED, Guerrero-Sánchez VM, Hernández-Lao T, Rey MD, Jorrín-Novo JV, Castillejo MA.   +6 more
europepmc   +1 more source

Trees, Forests and Rearranging [PDF]

The Computer Journal, 1960
openaire   +2 more sources

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

Origin and cohabitation of arbuscular mycorrhizal fungi matter: forest-derived Gigaspora species are promising candidates for bioinoculant development in forest tree production. [PDF]

Mycorrhiza
Djotan AKG, Matsuda Y, Matsushita N, Fukuda K.   +3 more
europepmc   +1 more source

Genomic insights into local adaptation and future climate-induced vulnerability of a keystone forest tree in East Asia. [PDF]

Nat Commun, 2022
Sang Y, Long Z, Dan X, Feng J, Shi T, Jia C, Zhang X, Lai Q, Yang G, Zhang H, Xu X, Liu H, Jiang Y, Ingvarsson PK, Liu J, Mao K, Wang J.   +16 more
europepmc   +1 more source

Artificial Intelligence for Bone: Theory, Methods, and Applications

Advanced Intelligent Discovery, EarlyView.
Advances in artificial intelligence (AI) offer the potential to improve bone research. The current review explores the contributions of AI to pathological study, biomarker discovery, drug design, and clinical diagnosis and prognosis of bone diseases. We envision that AI‐driven methodologies will enable identifying novel targets for drugs discovery. The
Dongfeng Yuan, Haicang Zhang, Liping Tong, Di Chen   +3 more
wiley   +1 more source

Lightweight Pest Object Detection Model for Complex Economic Forest Tree Scenarios. [PDF]

Insects
Cheng X, Wang X, Kang Y, Deng Y, Lu Q, Tang J, Shi Y, Zhao J.   +7 more
europepmc   +1 more source

Forest tree species distribution for Europe 2000-2020: mapping potential and realized distributions using spatiotemporal machine learning. [PDF]

PeerJ, 2022
Bonannella C, Hengl T, Heisig J, Parente L, Wright MN, Herold M, de Bruin S.   +6 more
europepmc   +1 more source

A Machine Learning Model for Interpretable PECVD Deposition Rate Prediction

Advanced Intelligent Discovery, EarlyView.
This study develops six machine learning models (k‐nearest neighbors, support vector regression, decision tree, random forest, CatBoost, and backpropagation neural network) to predict SiNx deposition rates in plasma‐enhanced chemical vapor deposition using hybrid production and simulation data.
Yuxuan Zhai, Wenrui Wang, Rui Chen, Tong Yu, Xiaojuan Zheng, Hua Shao, Dashan Shang, Ling Li, Lado Filipovic   +8 more
wiley   +1 more source

Mammalian Browsers Disrupt Eco-Evolutionary Dynamics in a Forest Tree Restoration Planting. [PDF]

Evol Appl
Costa E Silva J, Potts BM, Harrison PA.
europepmc   +1 more source