Polymeric and Polymer-Functionalized Drug Delivery Vectors: From Molecular Architecture and Elasticity to Cellular Uptake.

Polymers and polymer composites offer versatile possibilities for engineering the physico-chemical properties of materials on micro- and macroscopic scales. This review provides an overview of polymeric and polymer-decorated particles that can serve as drug-delivery vectors: linear polymers, star polymers, diblock-copolymer micelles, polymer-grafted nanoparticles, polymersomes, stealth liposomes, microgels, and biomolecular condensates. The physico-chemical interactions between the delivery vectors and biological cells range from chemical interactions on the molecular scale to deformation energies on the particle scale.

A simplified model of NMDA-receptor-mediated dynamics in leaky integrate-and-fire neurons.

A model for NMDA-receptor-mediated synaptic currents in leaky integrate-and-fire neurons, first proposed by Wang (J Neurosci, 1999), has been widely studied in computational neuroscience. The model features a fast rise in the NMDA conductance upon spikes in a pre-synaptic neuron followed by a slow decay. In a general implementation of this model which allows for arbitrary network connectivity and delay distributions, the summed NMDA current from all neurons in a pre-synaptic population cannot be simulated in aggregated form.

Exploiting ^{20}Ne Isotopes for Precision Characterizations of Collectivity in Small Systems.

Whether or not femto-scale droplets of quark-gluon plasma (QGP) are formed in so-called small systems at high-energy colliders is a pressing question in the phenomenology of the strong interaction. For proton-proton or proton-nucleus collisions the answer is inconclusive due to the large theoretical uncertainties plaguing the description of these processes. While upcoming data on collisions of ^{16}O nuclei may mitigate these uncertainties in the near future, here we demonstrate the unique possibilities offered by complementing ^{16}O+^{16}O data with collisions of ^{20}Ne ions.

The interplay between homeostatic synaptic scaling and homeostatic structural plasticity maintains the robust firing rate of neural networks.

Critical network states and neural plasticity enable adaptive behavior in dynamic environments, supporting efficient information processing and experience-dependent learning. Synaptic-weight-based Hebbian plasticity and homeostatic synaptic scaling are key mechanisms that enable memory while stabilizing network dynamics. However, the role of structural plasticity as a homeostatic mechanism remains less consistently reported, particularly under activity inhibition, leading to an incomplete understanding of its functional impact.

The baryon-baryon interaction in the large- limit.

We analyze the large- structure of the baryon-baryon potential derived in the framework of SU(3) chiral perturbation theory up to next-to-leading order including contact interactions as well as one-meson and two-meson exchange diagrams. Moreover, we assess the impact of SU(3) symmetry breaking from a large- perspective and show that the leading order results can successfully be applied to the hyperon-nucleon potential. Our results include a reduction of the number of relevant low-energy constants of the leading order contact interaction from fifteen to three, and we show that consistency is preserved if the / ratio is given by 2/3 and the / ratio for the baryon decuplet-to-octet coupling is given by 2.

NESTML: a generic modeling language and code generation tool for the simulation of spiking neural networks with advanced plasticity rules.

With increasing model complexity, models are typically re-used and evolved rather than starting from scratch. There is also a growing challenge in ensuring that these models can seamlessly work across various simulation backends and hardware platforms. This underscores the need to ensure that models are easily findable, accessible, interoperable, and reusable-adhering to the FAIR principles.

Activity-enhanced shear thinning of flexible linear polar polymers.

The rheological properties of tangentially propelled flexible polymers under linear shear flow are studied by computer simulations and are compared with analytical calculations. We find a significant impact of the coupled nonequilibrium active and shear forces on the polymer characteristics. The polar activity enhances shear-induced stretching along the flow direction, shrinkage in the transverse direction, and implies a strongly amplified shear-thinning behavior.

Run-and-tumble dynamics of is governed by its mechanical properties.

The huge variety of microorganisms motivates fundamental studies of their behaviour with the possibility to construct artificial mimics. A prominent example is the bacterium, which employs several helical flagella to exhibit a motility pattern that alternates between run (directional swimming) and tumble (change in swimming direction) phases. We establish a detailed model, coupled to fluid flow described by the dissipative particle dynamics method, and investigate its run-and-tumble behaviour.

Metadata practices for simulation workflows.

Computer simulations are an essential pillar of knowledge generation in science. Exploring, understanding, reproducing, and sharing the results of simulations relies on tracking and organizing the metadata describing the numerical experiments. The models used to understand real-world systems, and the computational machinery required to simulate them, are typically complex, and produce large amounts of heterogeneous metadata.

Arbor-TVB: A Novel Multi-Scale Co-Simulation Framework with a Case Study on Neural-Level Seizure Generation and Whole-Brain Propagation.

Computational neuroscience has traditionally focused on isolated scales, limiting understanding of brain function across multiple levels. While microscopic models capture biophysical details of neurons, macroscopic models describe large-scale network dynamics. Integrating these scales, however, remains a significant challenge.

Simplified two-compartment neuron with calcium dynamics capturing brain-state specific apical-amplification, -isolation and -drive.

Mounting experimental evidence suggests the hypothesis that brain-state-specific neural mechanisms, supported by the connectome shaped by evolution, could play a crucial role in integrating past and contextual knowledge with the current, incoming flow of evidence (e.g., from sensory systems).

Fine-tunings in nucleosynthesis and the emergence of life: status and perspectives.

We discuss the fine-tunings of nuclear reactions in the Big Bang and in stars and draw some conclusions on the emergence of the light elements and the life-relevant elements carbon and oxygen. We also stress how to improve these calculations in the future. This requires a concerted effort of different communities, especially in nuclear reaction theory, lattice QCD for few-nucleon systems, stellar evolution calculations, particle physics and philosophy.

Improving data sharing and knowledge transfer via the Neuroelectrophysiology Analysis Ontology (NEAO).

Describing the analysis of data from electrophysiology experiments investigating the function of neural systems is challenging. On the one hand, data can be analyzed by distinct methods with similar purposes, such as different algorithms to estimate the spectral power content of a measured time series. On the other hand, different software codes can implement the same analysis algorithm, while adopting different names to identify functions and parameters.

Lattice QCD Study of Pion Electroproduction and Weak Production from a Nucleon.

Quantum fluctuations in QCD influence nucleon structure and interactions, with pion production serving as a key probe of chiral dynamics. In this Letter, we present a lattice QCD calculation of multipole amplitudes at threshold, related to both pion electroproduction and weak production from a nucleon, using two gauge ensembles near the physical pion mass. We develop a technique for spin projection and construct multiple operators for analyzing the generalized eigenvalue problem in both the nucleon-pion system in the center-of-mass frame and the nucleon system with nonzero momentum.

Modelling motility of Trypanosoma brucei.

African trypanosomiasis, or sleeping sickness, is a life-threatening disease caused by the protozoan parasite Trypanosoma brucei. The bloodstream form of T. brucei has a slender body with a relatively long active flagellum, which makes it an excellent swimmer.

Active membrane deformations of a minimal synthetic cell.

Living cells can adapt their shape in response to their environment, a process driven by the interaction between their flexible membrane and the activity of the underlying cytoskeleton. However, the precise physical mechanisms of this coupling remain unclear. Here we show how cytoskeletal forces acting on a biomimetic membrane affect its deformations.

Intelligent soft matter: towards embodied intelligence.

Intelligent soft matter lies at the intersection of materials science, physics, and cognitive science, promising to change how we design and interact with materials. This transformative field aims to create materials with life-like capabilities, such as perception, learning, memory, and adaptive behavior. Unlike traditional materials, which typically perform static or predefined functions, intelligent soft matter can dynamically interact with its environment, integrating multiple sensory inputs, retaining past experiences, and making decisions to optimize its responses.

Fluctuation-Mediated Spin-Orbit Torque Enhancement in the Noncollinear Antiferromagnet MnNiCuN.

We report strong spin-orbit torques (SOTs) generated by noncollinear antiferromagnets MnNiCuN, over a wide temperature range. The SOT efficiency peaks up to 0.3 at the Néel temperature (), substantially higher than that of commonly studied nonmagnets, such as Pt.

Ab Initio Study of the Beryllium Isotopes ^{7}Be to ^{12}Be.

We present a systematic ab initio study of the low-lying states in beryllium isotopes from ^{7}Be to ^{12}Be using nuclear lattice effective field theory with the N^{3}LO interaction. Our calculations achieve good agreement with experimental data for energies, radii, and electromagnetic properties. We introduce a novel, model-independent method to quantify nuclear shapes, uncovering a distinct pattern in the interplay between positive and negative parity states across the isotopic chain.

Unveiling long-range forces in light-harvesting proteins: Pivotal roles of temperature and light.

Electrodynamic interactions between biomolecules are of potential biological interest for temporal and spatial molecular controls, warranting investigation of their activation through various mechanisms in living systems. Using a light-harvesting protein in the phycobilisome antenna system of red algae, we proved that not only light exposure but also thermal energy alone can trigger attractive electrodynamic interactions up to hundreds of nanometers, sustained by low-frequency collective modes. Activation of such modes and interactions might influence conformational rearrangements and energy transport within the phycobilisome system.

Genetic Variants and Clinical Phenotyping in 39 Pediatric Patients with Neuropathic Pain.

Pathogenic variants in voltage-gated sodium channels (VGSCs) may cause disturbed sensory function, including small fiber neuropathy (SFN) in adults, but little is known about their role in children and adolescents.A total of 39 prospectively enrolled children (age 12.03 ± 4.

Conformational properties of active polar semiflexible phantom polymers.

The conformational properties of semiflexible active polar linear and ring phantom polymers are analyzed analytically to shed light on their dependence on activity. Special attention is paid to the influence of the implemented bond force for discrete and continuous polymer models. In detail, the Gaussian semiflexible polymer model and a model with a harmonic bond potential with finite bond length are considered.

On the validity of electric brain signal predictions based on population firing rates.

Neural activity at the population level is commonly studied experimentally through measurements of electric brain signals like local field potentials (LFPs), or electroencephalography (EEG) signals. To allow for comparison between observed and simulated neural activity it is therefore important that simulations of neural activity can accurately predict these brain signals. Simulations of neural activity at the population level often rely on point-neuron network models or firing-rate models.

Classification of first recovery steps after quiet standing following external perturbation from different directions.

Recovery from external perturbations typically involves stepping, with the perturbation direction playing a key role in determining the recovery strategy. To date, classifications of these stepping strategies have relied on prior knowledge of perturbation direction, which is not always available when considering experimental paradigms close to real-world scenario. Here, we introduce a novel Unified classification method that enables the labeling of first recovery steps based solely on body kinematics.

Viscotaxis of beating flagella.

Many biological microorganisms and artificial microswimmers react to external cues of environmental gradients by changing their swimming directions. We study here the behavior of eukaryotic flagellated microswimmers in linear viscosity gradients. Motivated by the near-surface motion of many microswimmers, we consider flagellar swimming in two spatial dimensions.