The Development of Early Phonological Networks: An Analysis of Individual Longitudinal Vocabulary Growth.

While much work has emphasized the role of the environment in language learning, research equally reports consistent effects of the child's knowledge, in particular, the words known to individual children, in steering further lexical development. Much of this work is based on cross-sectional data, assuming that the words typically known to children at n months predict the words typically known to children at n+x months. Given acknowledged variability in the number of words known to individual children at different ages, a more conclusive analysis of this issue requires examination of individual differences in the words learned by individual children across development, that is, using longitudinal data.

Brownian diffusion in non-harmonic potentials.

Brownian motion in confined systems is widespread in soft matter physics, biophysics, statistical physics and related fields. In most of these systems, a Brownian particle cannot freely diffuse in the space but is confined by a potential well in a limited range of positions. When performing data analysis, typically the harmonic assumption is made, assuming that in the regions explored by the particle during its dynamics, the confining potential is fairly well described by a harmonic potential.

Dual-feature selectivity enables bidirectional coding in visual cortical neurons.

Sensory neurons are traditionally viewed as feature detectors that respond with an increase in firing rate to preferred stimuli while remaining unresponsive to others. Here, we identify a dual-feature encoding strategy in macaque visual cortex, wherein many neurons in areas V1 and V4 are selectively tuned to two distinct visual features-one that enhances and one that suppresses activity-around an elevated baseline firing rate. By combining neuronal recordings with functional digital twin models-deep learning-based predictive models of biological neurons-we were able to systematically identify each neuron's preferred and non-preferred features.

Effervescence in a binary mixture with nonlinear non-reciprocal interactions.

Non-reciprocal interactions between scalar fields that represent the concentrations of two active species are known to break the parity and time-reversal (PT) symmetries of the equilibrium state, as manifested in the emergence of travelling waves. We explore the notion of nonlinear non-reciprocity and consider a model in which the non-reciprocal interactions can depend on the local values of the scalar fields in such a way that the non-reciprocity can change sign. For generic cases where such couplings exist, we observe the emergence of spatiotemporal chaos in the steady-state.

Information-Optimal Mixing at Low Reynolds Number.

Mutual information between particle positions before and after mixing provides a universal assumption-free measure of mixing efficiency at low Reynolds number that accounts for the kinematic reversibility of the Stokes equation. For a generic planar shear flow with time-dependent shear rate, we derive a compact expression for the mutual information as a nonlinear functional of the shearing protocol and solve the associated extremization problem exactly to determine the optimal control under both linear and nonlinear constraints, specifically total shear and total dissipation per unit volume. Remarkably, optimal protocols turn out to be universal and time-reversal symmetric in both cases.

A roadmap for next-generation nanomotors.

Since their discovery in 2004, there has been remarkable progress in research on nanomotors, from the elucidation of different propulsion mechanisms to the study of their collective behaviour, culminating in investigations into their applications in biomedicine and environmental remediation. This Perspective reviews this evolution in nanomotor research and discusses the key challenges ahead, including the need for developing advanced characterization techniques, precise motion control, materials innovation, theory and modelling, and translationally feasible in vivo biomedical applications. These challenges highlight the current limitations of synthetic nanomotors and point to exciting future opportunities to revolutionize theranostics and create 'living' hybrid systems.

3D multiscale shape analysis of nuclei and in vivo elastic stress sensors allows force inference.

The measurement of stresses and forces at the tissue level has proven to be an indispensable tool for the understanding of complex biological phenomena such as cancer invasion, embryo development, or wound healing. One of the most versatile tools for force inference at the cell and tissue level are elastic force sensors, whose biocompatibility and tunable material properties make them suitable for many different experimental scenarios. The evaluation of those forces, however, is still a bottleneck due to the numerical methods seen in the literature until now, which are usually slow and render low experimental yield.

Isomerization of photo-Gb in phase-separated pore-spanning membranes alters Shiga toxin organization.

The lateral organization of Shiga toxin bound to a lipid membrane is significantly influenced by the fatty acid geometry of its receptor glycolipid globotriaosylceramide (Gb), which is crucial for the protein's internalization into the host cell. To control the lipid geometry, we used a photoisomerizable azobenzene derivative of Gb (photo-Gb) that can be switched between a trans- and a cis-configuration under light. We reconstituted this photo-Gb into liquid-disordered (l)/liquid-ordered (l) phase-separated pore-spanning membranes (PSMs), creating freestanding bilayer parts (freestanding PSMs [f-PSMs]) composed of an l phase surrounded by an l phase on the pore rims.

Crossover patterning through condensation and coarsening of pro-crossover factors.

Meiotic recombination mixes genetic information from parental genomes, creating unique combinations of alleles. During meiotic prophase, each homologue pair must undergo at least one crossover to segregate faithfully. Only a few recombination intermediates become crossovers, and these are widely spaced or limited to one per chromosome pair.

Learning to cluster neuronal function.

Deep neural networks trained to predict neural activity from visual input and behaviour have shown great potential to serve as digital twins of the visual cortex. Per-neuron embeddings derived from these models could potentially be used to map the functional landscape or identify cell types. However, state-of-the-art predictive models of mouse V1 do not generate functional embeddings that exhibit clear clustering patterns which would correspond to cell types.

Self-Propulsion and a Push-Pull Mechanism in Sessile Droplets.

Self-propelled droplets on solid substrates can autonomously move by physical forces, such as surface tension. This phenomenon mimics natural processes, such as the crawling of living cells or the propulsion of oil droplets on water, and provides valuable insights that apply to both natural phenomena and microfluidic applications. In this study, the self-propulsion of the evaporating droplet by the surface tension gradient on a polymer-coated substrate is investigated.

Success rates of simulated multi-pulse defibrillation protocols are sensitive to application timing with individual, protocol-specific optimal timings.

Ventricular fibrillation is a lethal condition where the heartbeat becomes too disorganised to maintain proper circulation. It is treated with defibrillation, which applies an electric shock in an attempt to reset the heart rhythm. As the high energy of this shock risks long-term harm to the patient, means of reducing it without compromising treatment efficacy are of great interest.

Hydrodynamically Consistent Many-Body Harada-Sasa Relation.

The effect of hydrodynamic interactions on the nonequilibrium stochastic dynamics of particles-arising from the conservation of momentum in the fluid medium-is examined in the context of the relationship between fluctuations, response functions, and the entropy production rate. The multiplicative nature of the hydrodynamic interactions is shown to introduce subtleties that preclude a straightforward extension of the Harada-Sasa relation. A generalization of the definitions involved in the framework is used to propose a new form of the relation applicable to systems with hydrodynamic interactions.

Topological phase locking in stochastic oscillators.

The dynamics of many nanoscale biological and synthetic systems such as enzymes and molecular motors are activated by thermal noise, and driven out-of-equilibrium by local energy dissipation. Because the energies dissipated in these systems are comparable to the thermal energy, one would generally expect their dynamics to be highly stochastic. Here, by studying a thermodynamically-consistent model of two coupled noise-activated oscillators, we show that this is not always the case.

Nonmonotonic Motion of Sliding Droplets on Strained Soft Solids.

Soft materials are ubiquitous in technological applications that require deformability, for instance, in flexible, water-repellent coatings. However, the wetting properties of prestrained soft materials are only beginning to be explored. Here we study the sliding dynamics of droplets on prestrained soft silicone gels, both in tension and in compression.

Electrolyte droplet spraying in H bubbles during water electrolysis under normal and microgravity conditions.

Electrolytically generated gas bubbles can significantly hamper the overall electrolysis efficiency. Therefore it is crucial to understand their dynamics in order to optimise water electrolyzer systems. Herein, we elucidate a distinct transport mechanism whereby electrolyte droplets are sprayed into H bubbles.

Enhancing reservoir predictions of chaotic time series by incorporating delayed values of input and reservoir variables.

Time series generated by chaotic dynamical systems can be effectively predicted using readouts from driven reservoir dynamics. In practical scenarios, however, only time series measurements with partial knowledge of the chaotic system's state are usually available. To address this aspect, we evaluate and compare the performance of reservoir computing in predicting time series under both conditions of complete and partial knowledge of the state.

Laves Phases Emerge in Neat AB-Type Block Copolymer as Hybrid Spherical Phases.

Soft spherical domains assembled from block copolymers can pack into Frank-Kasper phases, reminiscent of those found in alloys. As a special subcategory of Frank-Kasper phases, Laves phases are particularly challenging to obtain. So far, the most stable Laves phases have been realized in copolymer blends, where mixing different polymers accommodates the size discrepancy between the two types of spheres.

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.

Sensitive particle shape dependence of growth-induced mesoscale nematic structure.

Directed growth, anisotropic cell shapes, and confinement drive self-organization in multicellular systems. We investigate the influence of particle shape on the distribution and dynamics of nematic microdomains in a minimal model of proliferating, sterically interacting particles, akin to colonies of rod-shaped bacteria. By introducing continuously tuneable tip variations around a common rod shape with spherical caps, we find that subtle changes significantly impact the emergent dynamics, leading to distinct patterns of microdomain formation and stability.

Enhanced Stability and Chaotic Condensates in Multispecies Nonreciprocal Mixtures.

Random nonreciprocal interactions between a large number of conserved densities are shown to enhance the stability of the system toward pattern formation. The enhanced stability is an exact result when the number of species approaches infinity and is confirmed numerically by simulations of the multispecies nonreciprocal Cahn-Hilliard model. Furthermore, the diversity in dynamical patterns increases with an increasing number of components, and novel steady states such as pulsating or spatiotemporally chaotic condensates are observed.

Synchronization in epithelial tissue morphogenesis.

Coordination of cell behavior is central to morphogenesis, when arrays of cells simultaneously undergo shape changes or dynamic rearrangements. In epithelia, cell shape changes invariably exert mechanical forces, which adjacent cells could sense to trigger an active response. However, molecular mechanisms for such mechano-transduction and especially their role for tissue-wide coordination in morphogenesis have remained ambiguous.

Computer vision for primate behavior analysis in the wild.

Advances in computer vision and increasingly widespread video-based behavioral monitoring are currently transforming how we study animal behavior. However, there is still a gap between the prospects and practical application, especially in videos from the wild. In this Perspective, we aim to present the capabilities of current methods for behavioral analysis, while at the same time highlighting unsolved computer vision problems that are relevant to the study of animal behavior.

Foundation model of neural activity predicts response to new stimulus types.

The complexity of neural circuits makes it challenging to decipher the brain's algorithms of intelligence. Recent breakthroughs in deep learning have produced models that accurately simulate brain activity, enhancing our understanding of the brain's computational objectives and neural coding. However, it is difficult for such models to generalize beyond their training distribution, limiting their utility.