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Integrated modeling of groundwater–surface water interactions to evaluate management strategies for the protection of the Anzali coastal wetland
Breaking high-temperature dielectric energy storage limits through suppression of charge carrier transport
Editorial Expression of Concern: Integrating bioinformatics and experimental validation to Investigate IRF1 as a novel biomarker for nucleus pulposus cells necroptosis in intervertebral disc degeneration
MIND: multimodal integration with neighbourhood-aware distributions
Abstract Multimodal data integration combines different data modalities to improve predictive and classification performance. In biology, multi-omics profiling has become a powerful tool for applications such as cancer patient stratification. However, integration of multi-omics data remains challenging because of missingness and inherent heterogeneity. Methods such as imputation and sample exclusion often rely on strong assumptions that could lead to information loss or distortion. To address these limitations, we propose MIND (Multimodal Integration with Neighbourhood-aware Distributions), which learns patient-specific embeddings from incomplete multi-omics data using a multimodal Variational Autoencoder with a data-driven prior. We inject neighbourhood structure of the observed dataset, encoded as affinity matrices, into the prior, penalising latent configurations when neighbourhood structures in data and latent spaces diverge. MIND handles high missing rates, unbalanced missingness patterns, and low signal-to-noise ratios robustly. Compared with existing integration methods, MIND achieves better performance on downstream tasks on both synthetic and real data.
Assessment of the condition of railway substructure by developing performance indicators based on data from multiple sources
Ionic self-assembled monolayers enable neutral interfaces and synergistic charge extraction in high-efficiency perovskite solar cells
RGS22 is a metazoa-specific radial spoke component required for coordinated ciliary beating
Coherent control of quantum-dot spins with cyclic optical transitions
Abstract Solid-state spins are promising as interfaces from stationary qubits to single photons for quantum communication technologies. Semiconductor quantum dots have excellent optical coherence, exhibit near-unity collection efficiencies when coupled to photonic structures, and possess long-lived spins for quantum memory. However, the incompatibility of performing optical spin control and single-shot readout simultaneously has been a challenge faced by almost all solid-state emitters. To overcome this, we leverage light-hole mixing to realize a highly asymmetric lambda system in a negatively charged heavy-hole exciton in Faraday configuration. By compensating GHz-scale differential Stark shifts, induced by unequal coupling to Raman control fields, and by performing nuclear-spin cooling, we achieve quantum control of an electron-spin qubit with a π -pulse contrast of 97.4% while preserving spin-selective optical transitions with a cyclicity of 471 (50). We demonstrate this scheme for both GaAs and InGaAs quantum dots, and show that it is compatible with the operation of a nuclear quantum memory. Our approach thus enables repeated emission of indistinguishable photons together with qubit control, as required for single-shot readout, photonic cluster-state generation, and quantum repeater technologies.
Electrothermal vacuum sublimation drying of graphene aerogels for high-temperature synthesis
Development of response-controlled tall buildings through own-mass mobilisation
Abstract This paper describes a structural response reduction approach that, rather than adding mass as in conventional mass damping techniques, takes advantage of the large inherent mass present within a building and uses its own mass to generate large levels of reliable damping with minimal differential movements and without the need for frequency tuning. The proposed concept challenges the traditional premise of conceiving tall buildings as rigid, static entities, and exploits movements to improve performance. The underlying premise is investigated using purpose-devised aeroelastic wind tunnel tests on a dynamically scaled model of a 300-metre prototype building in which the damping system is explicitly incorporated. The results show reductions exceeding 70% in peak accelerations and 50% in base moments, relative to a conventional undamped configuration, while maintaining differential displacements between the movable floors and the core below 50 mm under the 50-year return wind. The results of the wind tunnel tests, coupled with complementary numerical studies as well seismic time-history simulations, demonstrate the considerable promise of the proposed system for significantly reducing the demands on the superstructure and foundation. This opens the door for low-carbon, high-performance tall building designs that can enable more resilient and sustainable urban development.
Stable readout of visual representations mediates flexible generalization
hexABC seeking the physical code of DNA
Spiking neural network decoders of finger forces from high-density intramuscular microelectrode arrays
Abstract Assistive technologies for restoring naturalistic finger control require continuous and robust decoding of motor intent, with high accuracy and low latency. Here, we present a spike-based decoding framework that exploits the dynamics of spiking neural networks (SNNs) to efficiently process motor unit activity extracted from high-density intramuscular microelectrode arrays. Using this framework, we demonstrate simultaneous and proportional decoding of individual finger forces from motor unit spike trains during isometric contractions at 15% of maximum voluntary contraction. We systematically evaluated the properties of different SNN decoder configurations, comparing two possible input modalities: physiologically grounded motor unit spike trains and spike-encoded intramuscular EMG signals. Through this comparison, we determined the trade-offs between decoding accuracy, memory footprint, and robustness to input errors. Our results show that lean shallow SNNs are sufficient to decode finger-level motor intent with competitive accuracy, while operating, with minimal memory requirements and without the need for external pre-processing modules. This work provides a practical blueprint for integrating compact, low-power and low-latency SNNs into finger-level force decoding systems, demonstrating how the choice of input representation can be strategically tailored to meet application-specific requirements for accuracy, robustness, and memory efficiency.