搜索结果：biosystems

Coarse-grained (CG) force field methods for molecular systems are a crucial tool to simulate large biological macromolecules and are therefore essential for characterisations of biomolecular systems. While state-of-the-art deep learning (DL)-based models for all-atom force fields have improved immensely over recent years, we observe and analyse significant limitations of the currently available approaches for DL-based CG simulations. In this work, we present the first transferable DL-based CG force field approach (i.e., not specific to only one narrowly defined system type) applicable to a wide range of biosystems. To achieve this, our CG algorithm does not rely on hard-coded rules and is tuned to output coarse-grained systems optimised for minimal statistical noise in the ground truth CG forces, which results in significant improvement of model training. Our force field model is also the first CG variant that is based on the MACE architecture and is trained on a custom dataset created by a new approach based on the fragmentation of large biosystems covering protein, RNA and lipid chemistry. We demonstrate that our model can be applied in molecular dynamics simulations to obtain st

Mechanosensitive ion nanochannels regulate transport by undergoing conformational changes within nanopores. However, achieving precise control over these conformational states remains a major challenge for both artificial soft or solid pores. Here, we propose an alternative mechanism that modulates the charge carrier density inside nanopores, inspired by transistors in solid-state electronics. This strategy leverages a novel phenomenon of confinement-regulated ion clustering in two-dimensional extremely confined nanochannels, revealed by extensive $μ$s-scale enhanced-sampling molecular simulations based on an \emph{ab initio}-refined force field and nucleation theory. The resulting \emph{force-ion transistor} enables mechanically gated control of ion transport and provides a conceptual foundation for designing ionic mechanical logic gates. Our findings offer new insights into piezochannel mechanosensing and electromechanical coupling in biosystems beyond conformational signaling, opening pathways to integrate artificial ion channels with neuromorphic devices for processing mechanical stimuli.

Performing cell-free expression (CFE) in tailored microfluidic environments is a powerful tool to investigate the organisation of biosystems from molecular to multicellular scales. While cell-free transcription-translation systems simplify and open up cellular biochemistry for manipulation, microfluidics enables miniaturisation and precise control over geometries and reaction conditions. In this review, we highlight the benefits of combining microfluidics with CFE reactions for the study and engineering of molecular functions and the construction of life-like systems from non-living components. By defining spatial organisation at different scales and sustaining non-equilibrium conditions, microfluidic environments play a key role in the quest to boot up the biochemistry of life.

Chirality is a pervasive property of matter that underpins many important phenomena across physics, chemistry and biology. Given its broad significance, the development of protocols for rational control of chirality in solid state systems is highly desirable, especially if this effect can be tuned continuously and in two directions. Yet, this goal has remained elusive due to the absence of a universal conjugate field that couples linearly to this structural order. Here, we introduce the piezochiral effect, which enables control of chirality through mechanical strain. We first show by symmetry analysis that uniaxial strain induces chirality in a broad class of achiral crystals that host fragments of opposite chirality within each unit cell, an effect that has so far remained unrecognized. The strain-induced handedness can be tuned either by changing the strain direction or by switching between compressive and tensile strain. We experimentally verify this effect in AgGaS2, using measurements of the optical activity under strain. Our discovery establishes a new scheme for chirality control, with potential applications that range from spintronics to asymmetric catalysis, and enantiosel

We extend a formal framework that previously derived time from the multifractal structure of biological lineages (Hudnall \& D'Souza, 2025). That work showed that time itself is multifractal -- not a universal background dimension, but an observer-dependent geometry. Here we develop the corresponding theory of measurement: showing that a multifractal conception of time not only permits measurement, but grounds it more rigorously in the structure of biology. The tree of life is modeled as the outcome of stochastic, convex branching, and we show how information-theoretic and fractal measures render its multifractal geometry into measurable, observer-relative time intervals. At the core is a dilation equation that expresses relative time elapse between entities as dimensionless ratios. Operational standards such as the SI second remain valid, but our framework makes explicit their lineage-dependence. This framework unifies measurement theory with biological form, preserves full compatibility with established science, and provides a biologically grounded theory of observation. It enables comparative analyses of duration and kinematics across lineages, with predictions that are dire

Manual fruit harvesting is common in agriculture, but the amount of time pickers spend on non-productive activities can make it very inefficient. Accurately identifying picking vs. non-picking activity is crucial for estimating picker efficiency and optimising labour management and harvest processes. In this study, a practical system was developed to calculate the efficiency of pickers in commercial strawberry harvesting. Instrumented picking carts (iCarritos) were developed to record the harvested fruit weight, geolocation, and iCarrito movement in real time. The iCarritos were deployed during the commercial strawberry harvest season in Santa Maria, CA. The collected data was then used to train a CNN-LSTM-based deep neural network to classify a picker's activity into "Pick" and "NoPick" classes. Experimental evaluations showed that the CNN-LSTM model showed promising activity recognition performance with an F1 score of 0.97. The recognition results were then used to compute picker efficiency and the time required to fill a tray. Analysis of the season-long harvest data showed that the average picker efficiency was 75.07% with an estimation accuracy of 97.23%. Furthermore, the aver

The ability to design artificial micro/nanomachines able to perform sophisticated tasks crucially depends on the understanding of their interaction with biosystems and their compatibility with the biological environment. Here, Janus colloids fuelled only by glucose and light were designed, which can autonomously interact with cell-like compartments and trigger endocytosis. The crucial role played by the far field hydrodynamic interaction arising from the puller/pusher swimming mode and adhesion is evidenced. It is shown that a large contact time between the active particle and the lipid membrane is required to observe the engulfment of a particle inside a floppy giant lipid vesicle. Active Janus colloids showing relatively small velocities and a puller type swimming mode are able to target giant vesicles, deform their membranes and subsequently get stably engulfed. An instability arising from the unbound membrane segment is responsible for the transition between partial and complete stable engulfment. These experiments shed light on the physical criteria required for autonomous active particle engulfment in giant vesicles, which can serve as general principles in disciplines rangin

The aim of this review is to highlight the possibility to apply the mathematical formalism and methodology of quantum theory to model behaviour of complex biosystems, from genomes and proteins to animals, humans, ecological and social systems. Such models are known as quantum-like and they should be distinguished from genuine quantum physical modeling of biological phenomena. One of the distinguishing features of quantum-like models is their applicability to macroscopic biosystems, or to be more precise, to information processing in them. Quantum-like modeling has the base in quantum information theory and it can be considered as one of the fruits of the quantum information revolution. Since any isolated biosystem is dead, modeling of biological as well as mental processes should be based on theory of open systems in its most general form -- theory of open quantum systems. In this review we advertise its applications to biology and cognition, especially theory of quantum instruments and quantum master equation. We mention the possible interpretations of the basic entities of quantum-like models with special interest to QBism is as may be the most useful interpretation.

Exciton transfer along a polymer is essential for many biological processes, for instance light harvesting in photosynthetic biosystems. Here we apply a new witness of non-classicality to this phenomenon, to conclude that, if an exciton can mediate the coherent quantum evolution of a photon, then the exciton is non-classical. We then propose a general qubit model for the quantum transfer of an exciton along a polymer chain, also discussing the effects of environmental decoherence. The generality of our results makes them ideal candidates to design new tests of quantum features in complex bio-molecules.

Soft interfaces are ubiquitous in nature, governing quintessential hydrodynamics functions, like lubrication, stability and cargo transport. It is shown here how a magnetic force field at a magnetic-nonmagnetic fluid interface results in an ultra-soft interface with nonlinear elasticity and tunable viscous shear properties. The balance between magnetic pressure, viscous stress and Laplace pressure results in a deformed and stable liquid-in-liquid tube with apparent elasticity in the range 2 kPa -10 kPa, possibly extended by a proper choice of liquid properties. Such highly deformable liquid-liquid interfaces of arbitrary shape with vanishing viscous shear open doors to unique microfluidic phenomena, biomaterial flows and complex biosystems mimicking.

Silicon-vacancy (SiV) centers in diamond are a promising candidate for all-optical nanoscale high-sensitivity thermometry because they have sufficient sensitivity to reach the subkelvin precision required for application to biosystems. It is expected that nanodiamonds with SiV centers can be injected into cells to measure the nanoscale local temperatures of biosystems such as organelles. However, the smallest particle size used to demonstrate thermometry using SiV centers is a few hundred nanometers. We recently developed SiV-center-containing nanodiamonds via a detonation process that is suitable for large-scale production. Here, we investigate the spectral response of SiV-center-containing detonation nanodiamonds (SiV-DNDs) to temperature. We used air-oxidized and polyglycerol-coated SiV-DNDs with a mean particle size of around 20 nm, which is the smallest size used to demonstrate thermometry using color centers in nanodiamond. We found that the zero-phonon line for SiV-DND is linearly red-shifted with increasing temperature in the range of 22.0 to 40.5 $^\circ C$. The peak sensitivity to temperature was 0.011 $\pm$ 0.002 nm/K, which agrees with the reported high sensitivity of S

Structured illumination can reject out-of-focus signal from a sample, enabling high-speed and high-contrast imaging over large areas with widefield detection optics. Currently, this optical-sectioning technique is limited by image reconstruction artefacts and the need for sequential imaging of multiple colour channels. We combine multicolour interferometric pattern generation with machine-learning processing, permitting high-contrast, real-time reconstruction of image data. The method is insensitive to background noise and unevenly phase-stepped illumination patterns. We validate the method in silico and demonstrate its application on diverse specimens, ranging from fixed and live biological cells to synthetic biosystems, imaging at up to 37 Hz across a 44 x 44 $μm^2$ field of view.