Finding factors that enforce the multifaceted functions of FAMA
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Co-option and neofunctionalization of stomatal executors for defence against herbivores in Brassicales
Co-option of gene regulatory networks leads to the acquisition of new cell types and tissues. Stomata, valves formed by guard cells (GCs), are present in most land plants and regulate CO2 exchange. The transcription factor (TF) FAMA globally regulates GC differentiation. In the Brassicales, FAMA also promotes the development of idioblast myrosin cells (MCs), another type of specialized cell along the vasculature essential for Brassicales-specific chemical defences. Here we show that in Arabidopsis thaliana, FAMA directly induces the TF gene WASABI MAKER (WSB), which triggers MC differentiation. WSB and STOMATAL CARPENTER 1 (SCAP1, a stomatal lineage-specific direct FAMA target), synergistically promote GC differentiation. wsb mutants lacked MCs and the wsb scap1 double mutant lacked normal GCs. Evolutionary analyses revealed that WSB is conserved across stomatous angiosperms. We propose that the conserved and reduced transcriptional FAMA–WSB module was co-opted before evolving to induce MC differentiation.
Free mobility across group boundaries promotes intergroup cooperation
Group cooperation is a cornerstone of human society, enabling achievements that surpass individual capabilities. However, groups also define and restrict who benefits from cooperative actions and who does not, raising the question of how to foster cooperation across group boundaries. This study investigates the impact of voluntary mobility across group boundaries on intergroup cooperation. Participants, organized into two groups, decided whether to create benefits for themselves, group members, or everyone. In each round, they were paired with another participant and could reward the other’s actions during an ‘enforcement stage’, allowing for indirect reciprocity. In line with our preregistered hypothesis, when participants interacted only with in-group members, indirect reciprocity enforced group cooperation, while intergroup cooperation declined. Conversely, higher intergroup cooperation emerged when participants were forced to interact solely with out-group members. Crucially, in the free-mobility treatment – where participants could choose whether to meet an in-group or an out-group member in the enforcement stage – intergroup cooperation was significantly higher than when participants were forced to interact only with in-group members, even though most participants endogenously chose to interact with in-group members. A few ‘mobile individuals’ were sufficient to enforce intergroup cooperation by selectively choosing out-group members, enabling indirect reciprocity to transcend group boundaries. These findings highlight the importance of free intergroup mobility for overcoming the limitations of group cooperation.
The design space of E(3)-equivariant atom-centred interatomic potentials
Molecular dynamics simulation is an important tool in computational materials science and chemistry, and in the past decade it has been revolutionized by machine learning. This rapid progress in machine learning interatomic potentials has produced a number of new architectures in just the past few years. Particularly notable among these are the atomic cluster expansion, which unified many of the earlier ideas around atom-density-based descriptors, and Neural Equivariant Interatomic Potentials (NequIP), a message-passing neural network with equivariant features that exhibited state-of-the-art accuracy at the time. Here we construct a mathematical framework that unifies these models: atomic cluster expansion is extended and recast as one layer of a multi-layer architecture, while the linearized version of NequIP is understood as a particular sparsification of a much larger polynomial model. Our framework also provides a practical tool for systematically probing different choices in this unified design space. An ablation study of NequIP, via a set of experiments looking at in- and out-of-domain accuracy and smooth extrapolation very far from the training data, sheds some light on which design choices are critical to achieving high accuracy. A much-simplified version of NequIP, which we call BOTnet (for body-ordered tensor network), has an interpretable architecture and maintains its accuracy on benchmark datasets.
A MEMS traveling-wave micromotor-based miniature gyrocompass
Traditional gyrocompasses, while capable of providing autonomous directional guidance and path correction, face limitations in widespread applications due to their large size, making them unsuitable for compact devices. Microelectromechanical system (MEMS) gyrocompasses offer a promising alternative for miniaturization. However, current MEMS gyrocompasses require the integration of motor rotation modulation technology to achieve high-precision north-finding, whereas conventional motors in previous research introduce large volume and residual magnetism, thus undermining their size advantage. Here, we innovatively propose a miniature MEMS gyrocompass based on a MEMS traveling-wave micromotor, featuring the first integration of a chip-scale rotational actuator and combined with a precise multi-position braking control system, enabling high accuracy and fast north-finding. The proposed gyrocompass made significant advancements, reducing its size to 50 × 42.5 × 24.5 mm³ and achieving an azimuth accuracy of 0.199° within 2 min, which is half the volume of the smallest existing similar devices while offering twice the performance. These improvements indicate that the proposed gyrocompass is suitable for applications in indoor industrial robotics, autonomous driving, and other related fields requiring precise directional guidance.
Discovering fully semantic representations via centroid- and orientation-aware feature learning
Learning meaningful representations of images in scientific domains that are robust to variations in centroids and orientations remains an important challenge. Here we introduce centroid- and orientation-aware disentangling autoencoder (CODAE), an encoder–decoder-based neural network that learns meaningful content of objects in a latent space. Specifically, a combination of a translation- and rotation-equivariant encoder, Euler encoding and an image moment loss enables CODAE to extract features invariant to positions and orientations of objects of interest from randomly translated and rotated images. We evaluate this approach on several publicly available scientific datasets, including protein images from life sciences, four-dimensional scanning transmission electron microscopy data from material science and galaxy images from astronomy. The evaluation shows that CODAE learns centroids, orientations and their invariant features and outputs, as well as aligned reconstructions and the exact view reconstructions of the input images with high quality.
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