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By combining molecular dynamics simulations and topological analyses with scaling arguments, we obtain analytic expressions that quantitatively predict the entanglement length N_{e}, the plateau modulus G, and the tube diameter a in melts that span the entire range of chain stiffnesses for which systems remain isotropic. Our expressions resolve conflicts between previous scaling predictions for the loosely entangled [Lin-Noolandi, Gℓ_{K}^{3}/k_{B}T∼(ℓ_{K}/p)^{3}], semiflexible [Edwards-de Gennes: Gℓ_{K}^{3}/k_{B}T∼(ℓ_{K}/p)^{2}], and tightly entangled [Morse, Gℓ_{K}^{3}/k_{B}T∼(ℓ_{K}/p)^{1+ϵ}] regimes, where ℓ_{K} and p are, respectively, the Kuhn and packing lengths. We also find that maximal entanglement (minimal N_{e}) coincides with the onset of local nematic order.
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http://dx.doi.org/10.1103/PhysRevLett.124.147801 | DOI Listing |
Drugs Aging
September 2025
Dalla Lana School of Public Health, University of Toronto, V1 06, 2075 Bayview Avenue, Toronto, ON, M4N 3M5, Canada.
Background And Objectives: Older adults living with dementia are a heterogeneous group, which can make studying optimal medication management challenging. Unsupervised machine learning is a group of computing methods that rely on unlabeled data-that is, where the algorithm itself is discovering patterns without the need for researchers to label the data with a known outcome. These methods may help us to better understand complex prescribing patterns in this population.
View Article and Find Full Text PDFFront Comput Neurosci
August 2025
Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States.
Artificial neural networks are limited in the number of patterns that they can store and accurately recall, with capacity constraints arising from factors such as network size, architectural structure, pattern sparsity, and pattern dissimilarity. Exceeding these limits leads to recall errors, eventually leading to catastrophic forgetting, which is a major challenge in continual learning. In this study, we characterize the theoretical maximum memory capacity of single-layer feedforward networks as a function of these parameters.
View Article and Find Full Text PDFMol Nutr Food Res
September 2025
Laboratory of Bio-Analytical Chemistry, Research Institute of Pharmaceutical Sciences, Faculty of Pharmacy, Musashino University, Nishitokyo, Tokyo, Japan.
Health hazards caused by air pollutants are increasing worldwide (SDGs 3.9), but no established prevention methods exist. Recently, we showed that intraperitoneal administration of epigallocatechin gallate (EGCG) prevents air pollutant-induced acute lung injury.
View Article and Find Full Text PDFOncogene
September 2025
Department of Molecular Medicine and Biochemistry, Akita University Graduate School of Medicine, Akita, Japan.
Forkhead-box-protein P3 (FOXP3) is a key transcription factor in T regulatory cells (Tregs). However, its expression and significance in non-immune stromal cells in the tumor microenvironment remain unclear. Here, we demonstrated FOXP3 expression in stromal fibroblasts of mouse and human gastrointestinal tumors.
View Article and Find Full Text PDFEcotoxicol Environ Saf
September 2025
Department of Urology, Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan 646000, China. Electronic address:
Background: Prostate cancer (PRAD) is a common malignancy in men, and exposure to soil pollutants may contribute to its development. And exposure to soil pollutant has been linked to its development, as well as to other diseases including cardiovascular disorders, neurological conditions, and additional cancers.
Methods: This study integrates network toxicology, machine learning, and advanced technologies to investigate the mechanisms through which soil pollutants affect prostate cancer.