Wednesday, 4 November 2026: 2 new papers, 3 cross-lists and 1 replacement in Biological Physics (physics.bio-ph), in arXiv's order, with full abstracts. The same papers as arXiv's own listing, set as a newspaper; every title links to arXiv.
Among collective states of animal groups, midge swarms pose several puzzling questions. Their polarization order parameter is very small, and the insects interact only weakly with one another while remaining strongly bound to the swarm. In laboratory studies performed without external perturbations, the correlation length is small. By contrast, field studies reveal strong correlations, scale-free behavior, and power-law scaling of the correlation length, susceptibility, and correlation time. The dynamic correlation function exhibits data collapse only for short times when time is rescaled by the correlation time. Is there a theory that explains these disparate observations? We review several models proposed in the literature and extend our harmonically confined Vicsek model to anisotropic confinement. Within the extended criticality region of the phase diagram, numerical simulations predict elongated swarms and static critical exponents that lie between those of the two-dimensional and isotropic three-dimensional models. These values agree better with those measured in natural swarms. We also calculate probability density functions for both natural and laboratory swarms. Numerical simulations indicate that the laboratory distributions correspond to a different region of parameter space, although some open questions remain.
Lipid bilayers are central to cellular organization and function, yet their atomistic modelling remains constrained by its high computational cost. While coarse-grained (CG) models can overcome this bottleneck, they are limited in accuracy due to the use of simplified functional forms based on molecular mechanics. Using a hybrid functional form combining physics-based terms with a graph neural network, we present implicit-solvent, machine-learned CG models for POPC. When trained on single-lipid data, the model recovers the atomistic conformational landscape. By adding short atomistic simulations of a small flat bilayer to the training data and combining force matching with a denoising objective, we train a model that accurately reproduces bilayer structure and dynamics, in closer agreement with the atomistic reference than existing models, like Martini3 and DryMartini. Beyond its training data, the model stabilizes curved geometries, including vesicles, and captures complex cooperative phenomena such as bilayer and vesicle self-assembly and pore sealing. While limitations remain, particularly regarding chemical transferability, our results establish machine-learned coarse-grained potentials as a promising path towards robust and accurate CG models of lipids and membranes.
The dielectric properties of biological tissues are used to investigate the interaction of electromagnetic (EM) fields with the human body. These properties are integrated into EM numerical simulations, such as the finite difference time domain (FDTD) method, using dielectric models such as Cole-Cole and Debye models. These computational tools are employed to study the propagation of EM waves within the human body. This paper has developed the one-pole and two-pole Cole-Cole and Debye models for the dielectric properties of normal and diseased human trabecular bone tissue at microwave frequencies (0.5-8.5 GHz). The dielectric properties of normal trabecular and cortical bone were derived based on the literature, while those of diseased human trabecular bones (osteoporotic and osteoarthritis) were acquired from a previous study conducted by the authors, which characterised the properties within the 0.5-8.5 GHz frequency range. This study has employed a weighted least squares method to optimise the one-pole and two-pole dielectric models' parameters. The accuracy of the developed dielectric models is validated by comparing the simulated results with the experimental data already published by the authors. The comparative analysis of the models demonstrated that the one-pole Cole-Cole model achieved a better fit compared to the one-pole Debye model across all bone types. Additionally, the two-pole Cole-Cole model exhibited a better fit than the two-pole Debye model. The proposed models can be used in numerical simulations to investigate the interaction of EM fields with bone tissue and develop and optimise microwave bone health monitoring and treatment techniques.
Adrenal gland-induced hypertension results from underlying adrenal gland disorders including Conn's syndrome, Cushing's syndrome, and Pheochromocytoma. Due to the limitations of existing pharmaceutical and surgical treatment methods, minimally invasive treatment options like microwave ablation (MWA) have received significant attention for treating adrenal gland disorders. A precise comprehension of the dielectric properties of human adrenal glands will help to tailor energy delivery for MWA therapy, thus offering the potential to optimise treatments and minimise damage to surrounding tissues. This study reports the ex vivo dielectric properties of human adrenal glands, including the cortex, medulla, capsule, and tumours, based on the data obtained from four patients (diagnosed with Conn's syndrome, Cushing's syndrome, and Pheochromocytoma) who underwent unilateral adrenalectomy at the University Hospital Galway, Ireland. An open-ended coaxial probe measurement technique was used to measure the dielectric properties for a frequency range of 0.5-8.5 GHz. The dielectric properties were fitted using a two-pole Debye model, and a weighted least squares method was employed to optimise the model parameters. Moreover, the dielectric properties of adrenal tissues and tumours were compared across frequencies commonly used in MWA, including 915 MHz, 2.45 GHz, and 5.8 GHz. The study found that the dielectric properties of adrenal tumours were influenced by the presence of lipid-rich adenomas, and the dielectric properties of Cushing's syndrome tumour were lowest in comparison to the tumours in patients diagnosed with Conn's syndrome and Pheochromocytoma. Furthermore, a notable difference was observed in the dielectric properties of the medulla and cortex among patients diagnosed with Conn's syndrome, Cushing's syndrome, and Pheochromocytoma.
Self-assembly enables complex structures to emerge from simple building blocks through local interaction rules. Are some structures intrinsically easier to design, that is, do they admit more sets of assembly rules than others? And, can this be predicted from their geometry? We address these questions using a three-dimensional polycube model with programmable face-to-face binding, defining a structure's assembly rule probability as the chance that a randomly chosen rule set produces it. For polycubes, this probability decreases exponentially with assembly complexity, the minimum information required to specify a structure's assembly rules. Intuitively, this follows because structures with low assembly complexity are generated not only by minimal rule sets but also by numerous more elaborate ones, and so arise disproportionately often. Symmetry can reduce assembly complexity by allowing the same building blocks and local interactions to be reused across different parts of a structure. Indeed, we find that the highest probability structures come from a tiny subset of highly symmetric structures. However, symmetry alone does not determine assembly complexity. Structures with the same symmetry can differ substantially in the amount of assembly information they require, depending on how effectively their global symmetry can be exploited by local growth rules. Overall, our results link information, symmetry, and assembly rule probability and suggest that identifying which geometric features permit rule reuse is central to designing minimal programmable self-assembling systems.