Results 211 to 220 of about 49,726 (308)

First record of Penicilliopsis clavariiformis Solms. on seeds of Diospyros ferrea (Willd.) Bakh. from the Northern Western Ghats, India. [PDF]

Sci Rep
Bagam PH, Magdum AB, Shinde KV, Kumavat SS, Borde MY, Singh SK, Kamble SS, Nimbalkar MS.   +7 more
europepmc   +1 more source

Sampling Strategy: An Overlooked Factor Affecting Artificial Intelligence Prediction Accuracy of Peptides’ Physicochemical Properties

Advanced Intelligent Discovery, EarlyView.
This study reveals that sampling strategy (i.e., sampling size and approach) is a foundational prerequisite for building accurate and generalizable AI models in peptide discovery. Reaching a threshold of 7.5% of the total tetrapeptide sequence space was essential to ensure reliable predictions.
Meiru Yan, Ankeer Abuduhebaier, Haojin Zhou, Jiaqi Wang   +3 more
wiley   +1 more source

Unlocking Seed Dormancy and Elucidating Storage Behavior in <i>Morinda royoc</i> (Rubiaceae): Crucial Insights for Propagation and Ex Situ Germplasm Conservation. [PDF]

Biology (Basel)
Barrios D, Álvarez-Espino R.
europepmc   +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

Complete chloroplast genome and phylogenetic analysis of the korean endemic plant <i>Semiaquilegia quelpaertensis</i> D.C. Son & K.H. Lee (Isopyreae, Ranunculaceae). [PDF]

Mitochondrial DNA B Resour
Kang ES, Kim HB, Jang JE, Son DC.
europepmc   +1 more source

Toward Predictable Nanomedicine: Current Forecasting Frameworks for Nanoparticle–Biology Interactions

Advanced Intelligent Discovery, EarlyView.
Predictive models successfully screen nanoparticles for toxicity and cellular uptake. Yet, complex biological dynamics and sparse, nonstandardized data limit their accuracy. The field urgently needs integrated artificial intelligence/machine learning, systems biology, and open‐access data protocols to bridge the gap between materials science and safe ...
Mariya L. Ivanova, Michael Nicholls, Nicola Russo, Gueorgui Mihaylov, Konstantin Nikolic   +4 more
wiley   +1 more source

Proactive soft-failure prediction in optical transport networks via physics-inspired features and Infrastructure-as-Code orchestration. [PDF]

Sci Rep
Ali OM, Radwan AMA, Radwan OMA, Elsherbini MM.   +3 more
europepmc   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

A New Species of <i>Pycnospatha</i> (Araceae) from Eastern Thailand, with an Updated Key to All Known Species. [PDF]

Life (Basel)
Promprom W, Munglue P, Pasorn P, Lanorsavanh S, Chatan W.   +4 more
europepmc   +1 more source

Large‐Scale Machine Learning to Screen for Small‐Molecule Senolytics

Advanced Intelligent Discovery, EarlyView.
A consistent workflow underpins all experiments in this study. A dedicated model‐selection dataset first identifies optimal hyperparameters for each algorithm. Models are then trained and rigorously evaluated on independent sets of molecules using the senolytic ratio SR. Comprehensive hyperparameter exploration across SMILES representations, task types,
Alexis Dougha, Jesus Gil, Pedro J. Ballester   +2 more
wiley   +1 more source