Results 221 to 230 of about 289,272 (278)

From Natural Discovery to AI‐Guided Design: A Curated Collection of Compact Enhancers for Crop Engineering

Advanced Science, EarlyView.
ABSTRACT Precise transgene‐free gene upregulation remains a challenge in crop biotechnology, as conventional enhancers often exceed CRISPR‐mediated knock‐in size constraints and face regulatory hurdles. Here we establish a foundational cross‐species resource of compact transcriptional enhancers developed via STEM‐seq, a high‐throughput screening ...
Qi Yao, Jin Gao, Keling Wang, Yufeng Liu, Peijin Han, Hong Pan, Xiaofeng Yang, Qianlan Yin, Dating Zhong, Lu Ye, Qi Deng, Lingling Gao, Xiaoyu Tu, Dequan Wang, Yuming Lu   +14 more
wiley   +1 more source

Unveiling synchronization transitions in networks of coupled oscillators through persistent homology of local structures. [PDF]

Sci Rep
Zabaleta-Ortega Á, Masoller C, Guzmán-Vargas L.   +2 more
europepmc   +1 more source

Mini Review: Synergizing Driven Quantum Dynamics, AI, and Quantum Computing for Next-Gen Materials Science. [PDF]

J Phys Chem Lett
Akanbi OS, Shannon JP, Delhommelle J, Desgranges C.   +3 more
europepmc   +1 more source

A unified framework to explore soliton boundary interaction using topological magnetic soliton spring oscillators. [PDF]

Sci Rep
Qiao S, Zhou Y, Yan S, Quan Z, Yang W.
europepmc   +1 more source

A novel approach integrating topological deep learning from EEG Data in Alzheimer's disease. [PDF]

Sci Rep
Esteve M, Martinez-Gracia A, Rodríguez-Sala JJ, Brotons-Mas JR, Falcó A.   +4 more
europepmc   +1 more source

Topology Assisted Clustering of Temporal fMRI Brain Networks With Use-Case in Mitigating Non-Neural Multi-Site Variability. [PDF]

IEEE Access
Shovon AR, Kumar S, Deshpande G.
europepmc   +1 more source

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Topological Dynamic Matter

The Journal of Physical Chemistry Letters, 2020
The principles of topology in condensed matter physics have expanded to areas such as photonics, acoustics, electronics, and mechanics. Their extension to dynamic (soft) matter could enable the control and design of topological thermodynamic (micro)states and nonreciprocal dynamics, potentially leading to paradigmatic applications in molecular and ...
openaire   +2 more sources

Dynamic topology representing networks

Neural Networks, 2000
In the present paper, we propose a new algorithm, namely the Dynamic Topology Representing Networks (DTRN) for learning both topology and clustering information from input data. In contrast to other models with adaptive architecture of this kind, the DTRN algorithm adaptively grows the number of output nodes by applying a vigilance test. The clustering
J, Si, S, Lin, M A, Vuong
openaire   +2 more sources