98%
921
2 minutes
20
A sophisticated high-order framework nucleic acid (FNA) was engineered for the targeted delivery and responsive release of environment tolerant antisense peptide nucleic acids (asPNAs). The dendritic FNA-asPNAs system was constructed simple one-pot modular assembly and demonstrated a good synergistic effect with chemotherapy on drug resistant cancer cells.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1039/d3cc04298g | DOI Listing |
Sci Rep
September 2025
Department of Applied Mathematics, Faculty of Mathematics, Statistics, and Computer Science, University of Tabriz, Tabriz, 51666-16471, Iran.
Identifying governing equations in physical and biological systems from datasets remains a long-standing challenge across various scientific disciplines. Common methods like sparse identification of nonlinear dynamics (SINDy) often rely on precise derivative approximations, making them sensitive to data scarcity and noise. This study presents a novel data-driven framework by integrating high order implicit Runge-Kutta methods (IRKs) with the sparse identification, termed IRK-SINDy.
View Article and Find Full Text PDFSIAM J Math Data Sci
February 2025
Department of Mathematics, George Washington University, Washington, DC 20052 USA.
We introduce the supervised Gromov-Wasserstein (sGW) optimal transport, an extension of Gromov-Wasserstein that incorporates potential infinity entries in the cost tensor. These infinity entries enable sGW to enforce application-induced constraints on preserving pairwise distance to a certain extent. A numerical solver is proposed for the sGW problem and the effectiveness is demonstrated in various numerical experiments.
View Article and Find Full Text PDFSci Rep
September 2025
Sports Psychology, School of Arts and Sports, Hanyang University, Seoul, 04763, South Korea.
The growing use of artificial intelligence (AI) in physical education (PE) has led to an urgent need to develop robust methodologies that can be used to choose the most suitable algorithms in uncertain and vague environments. This paper introduces a new hybrid decision-making (DM) model that incorporates the weighted aggregated sum product assessment (WASPAS) technique into the q-rung linear Diophantine fuzzy set (q-RLDFS) framework. The primary objective is to address the gap in the lack of structured and uncertainty-resistant methods for assessing AI models based on multiple, frequently conflicting criteria in the domain of PE.
View Article and Find Full Text PDFCommun Eng
September 2025
Department of Disaster Mitigation for Structures, College of Civil Engineering, Tongji University, Shanghai, China.
Standard physics informed deep learning and their enhanced variants encounter challenges in addressing inverse problems characterized by extreme discontinuities and high-order parameterized differential equations due to the use of globally smooth activation functions, especially when the unknown parameters exhibit spatially distributed characteristics. Phenomena such as discontinuous loads, boundary truncations, and abrupt changes in material properties introduce singularities in the derivatives, which in turn lead to ill-conditioned information in the gradient flow. To address these limitations, here we propose an information-distilled physics-informed deep-learning framework that combines reduced-order modeling, multi-level domain decomposition, and an ill-conditioning-suppression mechanism.
View Article and Find Full Text PDFEntropy (Basel)
August 2025
INFN, Sezione di Bari, Dipartimento Interateneo di Fisica, Università degli Studi di Bari Aldo Moro, 70126 Bari, Italy.
Factor analysis is a well-known statistical method to describe the variability of observed variables in terms of a smaller number of unobserved latent variables called factors. Even though latent factors are conceptually independent of each other, their influence on the observed variables is often joint and synergistic. We propose to quantify the synergy of the joint influence of factors on the observed variables using O-information, a recently introduced metric to assess high-order dependencies in complex systems; in the proposed framework, latent factors and observed variables are jointly analyzed in terms of their joint informational character.
View Article and Find Full Text PDF