Machine-Learning Potential Molecular Dynamics Reveals the Critical Role of Flexibility in Solid-Liquid Nanofluidic Friction.

The confining walls made by 2D materials are often considered solid boundary conditions in studies of fluid transport through nanochannels, while the atomically thin walls inherently exhibit thermal fluctuations at a finite temperature. In this work, we investigate the solid-liquid interfacial friction properties of water confined within flexible nanochannels using machine-learning-potential molecular dynamics. Surprisingly, we find that the friction coefficient (λ) increases with lateral size in the flexible nanochannels, following a linear relationship with 1/, which is absent in rigid channels.

Hyperelastic characterization deep indentation.

Hyperelastic material characterization is crucial for sensing and understanding the behavior of soft materials-such as tissues, rubbers, hydrogels, and polymers-under quasi-static loading before failure. Traditional methods typically rely on uniaxial tensile tests, which require the cumbersome preparation of dumbbell-shaped samples for clamping in a uniaxial testing machine. In contrast, indentation-based methods, which are non-destructive and can be conducted without sample preparation, remain underexplored.

Nonlinear SPDEs and Maximal Regularity: An Extended Survey.

In this survey, we provide an in-depth exposition of our recent results on the well-posedness theory for stochastic evolution equations, employing maximal regularity techniques. The core of our approach is an abstract notion of critical spaces, which, when applied to nonlinear SPDEs, coincides with the concept of scaling-invariant spaces. This framework leads to several sharp blow-up criteria and enables one to obtain instantaneous regularization results.

When are Quarnets Sufficient to Reconstruct Semi-directed Phylogenetic Networks?

Phylogenetic networks are graphs that are used to represent evolutionary relationships between different taxa. They generalize phylogenetic trees since for example, unlike trees, they permit lineages to combine. Recently, there has been rising interest in semi-directed phylogenetic networks, which are mixed graphs in which certain lineage combination events are represented by directed edges coming together, whereas the remaining edges are left undirected.

Neural network-based fixed-time practical attitude synchronization control for uncertain networked spacecraft systems.

This paper investigates the distributed neural network (NN)-based fixed-time practical attitude synchronization of the networked spacecraft systems (NSSs) with model uncertainties and external disturbances. A novel practical attitude synchronization scheme is proposed using the Lagrangian representation of spacecraft attitude dynamics for both leaderless and leader-follower cases. Fixed-time control is first employed to enhance robustness against model uncertainties, and the NN is then integrated to advance its adaptability.

Dynamic Microstructured Thermoresponsive Interfaces for Label-Free Cell Sorting Based on Nonspecific Interactions.

Label-free cell sorting methods and materials are developed in this work. The microstructured thermoresponsive surfaces made of poly(glycidyl methacrylate) (PGMA) and poly(-isopropylacrylamide--glycidyl methacrylate) (PNIPAM--GMA) are prepared by phase separation on the submicron scale in thin films and then cross-linked and covalently grafted to the substrate. PGMA domains are used for cell adhesion, while the PNIPAM-co-GMA matrix pushes cells off the surface at a temperature below the lower critical solution temperature (LCST).

Efficacy of Poloxamer 188 in Experimental Myelosuppression Model Induced by Carboplatin in CBA Mice.

Poloxamer 188 is a polymer that is used as a carrier and stabilizer of pharmacological agents. It has been demonstrated to enhance red blood cell and hemoglobin levels in healthy animals and in select clinical cases. The objective of this study was to assess the efficacy of Poloxamer 188 in CBA mice when administered repeatedly in the carboplatin-induced myelosuppression model.

Immediate "sipping" vs. delayed oral fluid intake after extubation: A randomized controlled trial.

Purpose: Despite advances in perioperative care, delayed oral fluid intake after extubation remains common and is often based on tradition rather than evidence. This study aimed to evaluate whether immediate oral fluid intake "sipping" after extubation reduces thirst and discomfort and is safe in an intensive care setting.

Methods: In this single-center, prospective, randomized controlled trial, 160 ICU patients who met extubation criteria were randomized 1:1 to either delayed fluid intake (2 h post-extubation) or immediate sipping (up to 3 ml/kg over 2 h).

On the mathematical structure and numerical solution of discrete-continuous optimization problems in DDCM.

In this work, we investigate data-driven elasticity problems defined on a closed interval of the real line that are spatially discretized by means of the finite element method. This one-dimensional setting allows us to gain a deeper understanding of the underlying . We provide an in-depth analysis of their structural properties and prove their global solvability.

Predicting potato plant vigor from the seed tuber properties.

The vigor of potato plants is of crucial importance for potato seed producers, who are interested in predicting it at scale by exploiting the dependence of plant growth and development on the origin and physiological state of the seed tuber. In this article we present the results of a three-year long experiment in which we studied six potato varieties in three test fields. We identify a 73-[Formula: see text] overall correlation in the vigor of plants from the same seedlot grown in different test fields.

Transverse collective excitations in liquids: Is the phonon theory of liquid thermodynamics correct?

Nonhydrodynamic transverse excitations in liquids exhibit a propagation gap in the long-wavelength region of their dispersion curve. Using molecular dynamics simulations, we investigate the density dependence of the propagation gap width in simple liquids. Based on these findings, we critically analyze the so-called phonon theory of liquid thermodynamics by comparing its analytical predictions with simulation data for supercritical neon (Ne).

Dynamic Ester-Linked Vitrimers for Reprocessable and Recyclable Solid Electrolytes.

Traditional covalently cross-linked solid-state electrolytes exhibit desirable mechanical durability but suffer from limited processability and recyclability due to their permanent network structures. Incorporating dynamic covalent bonds offers a promising solution to these challenges. In this study, we report a reprocessable and recyclable polymer electrolyte based on a dynamic ester bond network, synthesized from commercially available materials.

Phylogroup Homeostasis of in the Human Gut Reflects the Physiological State of the Host.

The advent of alignment-free -mer barcoding has revolutionized taxonomic analysis, enabling bacterial identification at phylogroup resolution within natural communities. We applied this approach to characterize intraspecific diversity in human gut microbiomes using publicly available datasets representing diverse human physiological states. By estimating the relative abundance of eight phylogroups defined by their 18-mer markers in 558 fecal samples, we compared their distribution between gut microbiomes of healthy individuals, patients with chronic bowel diseases and volunteers subjected to various external interventions.

Formation of an Amyloid-like Structure During In Vitro Interaction of Titin and Myosin-Binding Protein C.

Protein association and aggregation are fundamental processes that play critical roles in a variety of biological phenomena from cell signaling to the development of incurable diseases, including amyloidoses. Understanding the basic biophysical principles governing protein aggregation processes is of crucial importance for developing treatment strategies for diseases associated with protein aggregation, including sarcopenia, as well as for the treatment of pathological processes associated with the disruption of functional protein complexes. This work, using a set of methods such as atomic force microscopy (AFM), transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FTIR), and X-ray diffraction, as well as bioinformatics analysis, investigated the structures of complexes formed by titin and myosin-binding protein C (MyBP-C).

Sensitivity of global storm surge modelling to sea surface drag.

Accurate storm surge modeling is essential for predicting coastal flooding and mitigating impacts on vulnerable regions. This study evaluates the influence of different sea surface drag parameterizations on surge predictions using the Global Tide and Surge Model (GTSM) over a 10-year period (2006-2015) and two storm events. Four model experiments were tested, ranging from a fully dynamic formulation, including variable air density, atmospheric stability, and sea-state-dependent drag, to a simplified constant-drag approach.

A refined sine-derived chaotic map for securing medical image encryption in telemedicine.

Telemedicine has revolutionized healthcare by enabling remote diagnostics and treatment; however, it also raises significant concerns regarding the security of transmitted medical data. In response, this paper introduces a novel encryption framework specifically designed to safeguard medical images during transmission and storage within telemedicine platforms. At the core of this framework lies a newly developed one-dimensional chaotic map, referred to as the IS map, which combines two sine functions with a Chebyshev polynomial to achieve enhanced chaotic behavior.

An algorithm for peptide de novo sequencing from a group of SILAC labeled MS/MS spectra.

Shotgun proteomics coupled with high-performance liquid chromatography and mass spectrometry has been instrumental in identifying proteins in complex mixtures. Effective computational approaches are required to automate the spectra interpretation process to handle the vast amount of data collected in a single Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) run. De novo sequencing from MS/MS has emerged as a vital technology for peptide sequencing in proteomics.

CAR-T cell therapy for glioblastoma: insight from mathematical modeling.

Introduction: Glioblastoma is a rare, aggressive brain tumor marked by high therapeutic resistance, poor prognosis, and limited treatment options. Emerging immunotherapies, particularly Chimeric Antigen Receptor (CAR) T-cell therapy, offer promising alternatives to standard care. However, adapting CAR-T cell strategies from hematologic malignancies to solid tumors like glioblastoma presents substantial challenges.

Deep operator network models for predicting post-burn contraction.

Background: Burn injuries present a significant global health challenge. Among the most severe long-term consequences are contractures, which can lead to functional impairments and disfigurement. Understanding and predicting the evolution of post-burn wounds is essential for developing effective treatment strategies.

Ab Initio Elucidation of the Nature of the Bonding of Tetrahedral Nitrides (BN, AlN, GaN, and InN), Hexagonal BN, and Graphene.

Recent measurements of the band properties of AlN and GaN by fluorescence yield absorption and soft X-ray emission spectroscopies revealed that their valence band (VB) is composed of two separate subbands. The upper VB subband of GaN is composed of gallium and nitrogen orbitals; the lower subband consists of metal and nitrogen orbitals. These findings were confirmed by extensive ab initio simulations.

LMS-Net: A learned Mumford-Shah network for binary few-shot medical image segmentation.

Few-shot semantic segmentation (FSS) methods have shown great promise in handling data-scarce scenarios, particularly in medical image segmentation tasks. However, most existing FSS architectures lack sufficient interpretability and fail to fully incorporate the underlying physical structures of semantic regions. To address these issues, in this paper, we propose a novel deep unfolding network, called the Learned Mumford-Shah Network (LMS-Net), for the FSS task.

Viscosity caused by self-gravity in the field of a massive body.

This study investigates self-gravity-induced viscosity in collisionless systems in the presence of a massive central body. Employing a kinetic framework based on the Vlasov-Poisson equations, we derive an analytical expression for viscosity arising from self-gravitational interactions. It is found that a self-gravitating medium in an external gravitational field exhibits non-Newtonian fluid behavior with anisotropic properties.

Droplet Impact-Based Microliter Viscometry.

Characterizing liquid viscosity and rheological properties with small sample volumes is crucial in fields where liquid samples are often limited, such as biological fluids for biomedical diagnostics and trace chemical products. However, traditional viscometers often require large sample volumes, and many existing small-volume viscometry techniques fall short in analyzing non-Newtonian fluids due to their limited shear rate range. While microfluidics-based viscometers offer flexibility in shear rate control, they are generally associated with intricate fabrication processes and high costs.

Role of tight junctions in three-dimensional mechanical model of blood-brain barrier.

The increasing prevalence of central nervous system (CNS) disorders has imposed a significant social and economic burden on healthcare systems. The blood-brain barrier (BBB) presents a major challenge for effective drug delivery to the brain, hindering disease treatment advancements. The BBB consists of various cell types, including microvascular endothelial cells and astrocytes, with tight junctions playing a key role in regulating molecular exchange and maintaining brain homeostasis.

Overlapping point cloud registration algorithm based on KNN and the channel attention mechanism.

With the advancement of sensor technologies such as LiDAR and depth cameras, the significance of three-dimensional point cloud data in autonomous driving and environment sensing continues to increase.Point cloud registration stands as a fundamental task in constructing high-precision environmental models, with particular significance in overlapping regions where the accuracy of feature extraction and matching directly impacts registration quality. Despite advancements in deep learning approaches, existing methods continue to demonstrate limitations in extracting comprehensive features within these overlapping areas.