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The measure of microscopic forces is currently dominated by optical methods requiring parameter-based analyses and long data acquisitions. This work describes a fast and parameter-free method that can characterize both the conservative and non-conservative force fields acting on Brownian particles.

Colloidal particles experience capillary interactions at liquid interfaces, but modifying these interactions is challenging as shape change is required. Here, the authors report polymer particles that change shape with polarised light, and therefore create flow patterns with unusual paths.

Indirect coordination among individuals through the environment typically requires some basic levels of communication and information processing. Dias et al. introduce a coordination mechanism that emerges in a population of clueless individuals, facilitated by environmental memory, culminating in group formation.

Single-molecule localisation microscopy enables nanoscale mapping of molecular organisation, but clustering stochastic data remains challenging. Here, authors present a graph neural network method that enhances clustering across complex biological datasets.

Miniaturizing mechanical machines is crucial for advancing nanotechnology. Here, authors present microscopic gear trains and micromachines powered by light-driven metasurfaces, achieving precise sub-micrometer control and sizes down to 10 μm. Their on-chip fabrication enables seamless integration and parallelization, offering new possibilities for micro- and nanoscale systems

The noise in a stochastic differential equation can be interpreted by Itô or by Stratonovich calculus, and which one to use has been a subject of discussion in statistical physics. Pesce et al.show that the underlying dynamics induce a shift from Stratonovic to Itô calculus in a noisy electrical circuit.

The incidental finding of small renal masses is becoming a more common event as a result of widely used cross-sectional imaging techniques. This review by Alessandro Volpe and Michael Jewett discusses the risks of tumor progression, techniques for assessing tumors and the different management options available to patients with renal cell carcinoma.

Researchers have developed retina electronic paper with electrically tunable metapixels comprising WO₃ nanodisks, achieving >25,000 pixels per inch, full-colour video and high contrast, enabling low-power immersive displays for virtual reality applications.

Muñoz-Gil and colleagues report the results of an open challenge where they benchmarked algorithms for the characterization of motion changes in single-particle tracking. By ranking methods on simulations, the competition revealed strengths and limitations of AI and classic approaches, guiding researchers toward optimal tools.

The capacity of brain networks to retain information during aging is crucial but not yet known. This study shows that the brain’s memory capacity, modelled with reservoir computing, offers new insights into aging, brain function and cognitive decline.

Many living systems, such as bacterial colonies, exhibit collective and dynamic behaviours that are sensitive to the change in environmental conditions. Here, the authors show that a colloidal active matter system switches between gathering and dispersal of individuals in response to a disordered potential.

Deviations from Brownian motion leading to anomalous diffusion are ubiquitously found in transport dynamics but often difficult to characterize. Here the authors compare approaches for single trajectory analysis through an open competition, showing that machine learning methods outperform classical approaches.

Recent advances in manipulating microscopic objects involve utilizing critical Casimir forces, controllable via temperature and chemical properties. By demonstrating the efficiency of critical Casimir torques, the authors enable precise alignment of microscopic objects on nanopatterned substrates.

The management of small renal masses is a subject of considerable controversy. Many benign masses are surgically removed, and evidence points to the slow growth rate of conservatively managed renal tumors, leading to the suggestion that we might be overtreating small renal masses. In the second of two Viewpoints examining this issue, the authors outline arguments in favor of surveillance therapy as an appropriate management option in selected patient groups.

The critical Casimir force, rising from fluctuating field confined between surfaces, is predicted to be nonadditive, but there is no experimental verification to date. Here the authors provide data support by quantifying the forces between three interacting colloidal particles using holographic traps.

Despite recent improvements in microscopy acquisition methods, extracting quantitative information from biological experiments in crowded conditions is a challenging task. Pineda and colleagues propose a geometric deep-learning-based framework for automated trajectory linking and dynamical property estimation that is able to effectively deal with complex biological scenarios.

For active particles with nanoscale dimensions the overwhelming rotational diffusivity usually masks their residual non-equilibrium character. Here Schmidt et al. show how to amplify it in a suitable experiment to let a nanosphere rotate spontaneously around the beam axis in an optical trap.

The authors demonstrate an optical trap where particles are trapped inside of a laser cavity. This is possible due to intracavity nonlinear feedback forces that produce stronger confinement on all 3 axes than standard optical tweezers, which greatly reduces the laser intensity needed to trap the same particle.

Casimir forces are normally attractive and cause stiction, that is, static friction preventing surfaces in contact from starting to move. Now, a system exhibiting tunable repulsive critical Casimir forces, relevant for the development of micro- and nanodevices, is demonstrated.

Object detection using machine learning universally requires vast amounts of training datasets. Midtvedt et al. proposes a deep-learning method that enables detecting microscopic objects with sub-pixel accuracy from a single unlabeled image by exploiting the roto-translational symmetries of the problem.

A Stereo-seq and scRNA-seq atlas of mouse liver in homeostasis and regeneration after partial hepatectomy identifies zonated genes, pathways, cell–cell interactions and gene regulatory networks. Functional validation finds that cooperation between TBL1XR1 and β-catenin activates hepatocyte proliferation.

Circuit and cell type prioritization for complex disorders is crucial to direct future efforts. Here the authors integrate GWAS, human single-cell RNA-seq and fMRI analysis suggesting multi-modality convergence on amygdalar and hippocampal circuits.

Nanofluidic scattering microscopy enables label-free, quantitative measurements of the molecular weight and hydrodynamic radius of biological molecules and nanoparticles freely diffusing inside a nanofluidic channel.

Different types of atrophy in Alzheimer’s disease may reflect different disease stages or biologically distinct subtypes. Here the authors use longitudinal neuroimaging data to demonstrate five distinct patterns of atrophy with different demographical and cognitive characteristics.

A large-scale single-cell transcriptomic atlas of the non-human primate Macaca fascicularis encompasses over 1 million cells from 45 adult tissues.

This Review discusses the state-of-the-art in optical trapping at the nanoscale, with an emphasis on some of the most promising advances such as controlled manipulation and assembly of individual and multiple nanostructures, force measurement with femtonewton resolution, and biosensors.

Detecting hydrogen gas in humid air is an unresolved challenge of significant importance for the safe implementation of hydrogen (energy) technologies. Here, authors demonstrate how the use of neural networks enables the sensing of hydrogen in highly humid air with a detection limit of 100 ppm.

Active matter can spontaneously form complex patterns and assemblies via a one-way energy flow from the environment into the system. Here, the authors demonstrate that a two-way coupling, where active particles act back on the environment can give rise to novel superstructures, named as active droploids.

Active colloidal systems can serve as an enabling platform to study complex out-of-equilibrium physical phenomena. Using a magnetic control with a feedback loop, here the authors program the dynamics of active Brownian particles by updating their rotational diffusion coefficient depending on their locations.

Cell type epigenetic and topographic information of primate brain is lacking. Here, authors identified transcriptional regulatory network, gradient expression pattern and disease vulnerability at cell type level in PFC, M1 and V1 of monkey brain by snRNAseq, snATAC-seq and Stereo-seq.

This Review surveys machine learning techniques that are currently developed for a range of research topics in biological and artificial active matter and also discusses challenges and exciting opportunities. This research direction promises to help disentangle the complexity of active matter and gain fundamental insights for instance in collective behaviour of systems at many length scales from colonies of bacteria to animal flocks.

Optomechanical effects enable the realization of optical metavehicles that can be manoeuvred across a surface in plane-wave illumination and steered by incident polarization.

Slow heterogeneous dynamics and the absence of visible structural order make it difficult to numerically and theoretically investigate glass-forming materials. This Technical Review outlines the role that machine learning tools can have and identifies key challenges, possible approaches and appropriate benchmarks.

In this Review, the authors discuss the unique neurological health disparities faced by sexual and gender minority (LGBT+) people. The Review presents clinical considerations alongside language and practice recommendations to promote inclusive care, and highlights the gaps in need of further research.