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Multiscale stress dynamics in sheared liquid foams revealed by tomo-rheoscopy
Abstract Rheology aims at quantifying the response of materials to mechanical forcing. However, standard rheometers provide only global macroscopic quantities, such as viscoelastic moduli. They fail to capture the heterogeneous flow of soft amorphous materials at the mesoscopic scale, arising from the rearrangements of the microstructural elements, that must be accounted for to build predictive models. To address this experimental challenge, we have combined shear rheometry and time-resolved X-ray micro-tomography on 3D liquid foams used as model soft jammed materials, yielding a unique access to the stresses and contact network topology at the bubble scale. We reveal a universal scaling behavior of the local stress build-up and relaxation associated with topological modifications. Moreover, these plastic events redistribute stress non-locally, as if the foam were an elastic medium subjected to a quadrupolar deformation. Our findings clarify how the macroscopic elastoplastic behavior of amorphous materials emerges from the spatiotemporal stress variations induced by microstructural rearrangements.
On Bearing Witness
The CCL20–integrin α5β1 interaction enhances TGF-β/Smad signaling to promote fibroblast activation in pulmonary fibrosis
Long-Term Safety and Efficacy of Gene Therapy for Adenosine Deaminase Deficiency
The neural basis for uncertainty processing in hierarchical decision making
Stercoral Colitis
Monopole-mediated light control of half skyrmion topology in nematic liquid crystals
The Contribution of Digital Treatment to Efforts to Reduce Global Tobacco Use
Zinc finger domains bind low-complexity domain polymers
Case 29-2025: A 43-Year-Old Woman with Depression, Suicidal Ideation, and Fever
Methylation reference datasets from quartet DNA materials for benchmarking epigenome sequencing
The AI Frontier in Humanitarian Aid — Embracing Possibilities and Addressing Risks
Targeting G1–S-checkpoint-compromised cancers with cyclin A/B RxL inhibitors
Predicting sequence-specific amplification efficiency in multi-template PCR with deep learning
Abstract Multi-template polymerase chain reaction (PCR) is a critical technique enabling the parallel amplification of diverse DNA molecules, thereby facilitating applications in fields from quantitative molecular biology to DNA data storage. However, non-homogeneous amplification due to sequence-specific amplification efficiencies often results in skewed abundance data, compromising accuracy and sensitivity. In this study, we address amplification efficiency in complex amplicon libraries by employing one-dimensional convolutional neural networks (1D-CNNs) to predict sequence-specific amplification efficiencies, based on sequence information alone. Trained on reliably annotated datasets derived from synthetic DNA pools, these models achieve a high predictive performance (AUROC: 0.88, AUPRC: 0.44), thereby enabling the design of inherently homogeneous amplicon libraries. We further introduce CluMo, a deep learning interpretation framework that identifies specific motifs adjacent to adapter priming sites as closely associated with poor amplification. This insight leads to the elucidation of adapter-mediated self-priming as the major mechanism causing low amplification efficiency, challenging long-standing PCR design assumptions. By addressing the basis for non-homogeneous amplification in multi-template PCR, our deep-learning approach reduces the required sequencing depth to recover 99% of amplicon sequences fourfold, and opens new avenues to improve the efficiency of DNA amplification in fields such as genomics, diagnostics, and synthetic biology.