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The bacterial flagellar type III secretion system (fT3SS) exports structural subunits required for flagellar assembly by coupling protein translocation to ion motive force across the cytoplasmic membrane. Efficient activation of the transmembrane export gate depends on a cytoplasmic ATPase complex composed of FliH, FliI, and FliJ, which are also involved in substrate delivery. However, how these proteins mechanistically integrate substrate delivery with gate activation remains unclear. Here, we uncoupled these two functions by cross-complementation analyses using ATPase components from the Na+-driven polar flagellum of Vibrio and the H+-driven flagellum of Salmonella. Despite low sequence identity, Vibrio FliJ complemented a Salmonella ΔfliJ mutant and restored Na+-independent protein export to a substantial extent, demonstrating a highly conserved mechanism of export gate activation. In contrast, Vibrio FliH and FliI exhibited interspecies incompatibility when expressed individually, and their co-expression in a Salmonella ΔfliH-fliI mutant supported protein export only under Na+-coupled conditions, consistent with the failure to activate the H+-driven export gate. Biochemical analyses revealed species-specific interactions between FliH and FliI, while high-speed atomic force microscopy showed that the Vibrio FliH-FliI complex retains the ability to assemble into ring-shaped structures. Together, these findings demonstrate that ATPase ring assembly and substrate delivery are mechanistically separable from export gate activation, revealing distinct and differentially conserved roles of the flagellar ATPase complex in coupling ATP hydrolysis to ion-driven protein export.

To protect the sluice gate substrate during laser cleaning of aged coatings, and to evaluate the cleaning effectiveness and surface quality after cleaning, this paper proposes a deep learning-based method for predicting process parameters for laser cleaning of sluice gate coatings. The proposed method introduces a learnable weighted decision layer (LWDL) based on fuzzy theory, which converts classification results obtained from images into multidimensional laser-cleaning process parameters. First, a sample dataset consisting of 194 images with a resolution of 2992×2992 pixels was established for the composite coating surface of the sluice gate. The samples were divided into 10 categories according to the laser-cleaning effectiveness, and data augmentation techniques were applied to expand the dataset and improve the model's generalization capability. Second, based on several existing high-performance classification network models, four network architectures suitable for this task were selected to construct an ensemble network framework. Subsequently, LWDL was introduced after the original classification networks, and the network models were trained using laser-cleaning process parameters obtained through theoretical calculations. Finally, the proposed model was deployed on an edge computing platform, and the end-to-end response time was tested to verify the real-time feasibility of the proposed scheme. In addition, the proposed method was compared with laser-cleaning approaches reported by different researchers in terms of cleaning effectiveness. Confocal microscopy was employed to systematically characterize the microscopic surface morphology of the cleaned sluice gate coating, thereby evaluating both the cleaning effectiveness and the degree of damage to the substrate material. The experimental results show that only a small amount of coating remains on the substrate surface, and the proposed continuous laser parameter adjustment scheme achieves a relatively better cleaning effect.

The Berkeley gate is a high-performance, two-qubit entangling operation with particular potential for quantum error correction and fault-tolerant protocols. However, harnessing this potential on current noisy intermediate-scale quantum (NISQ) processors, requires efficient compilation and robust performance under realistic noise conditions. In this work, we demonstrate a hardware-efficient implementation of the Berkeley gate on a superconducting quantum processor. Using quantum process tomography (QPT), we experimentally characterize its performance and benchmark it against a noiseless quantum simulator to evaluate its practical reliability in the NISQ era. Experimental measurements confirm the gate's correct logical action, producing the target partially entangled state with a subspace confinement probability of [Formula: see text] on real quantum hardware compared to 100% in quantum simulation. Results from QPT experiments show a simulated process fidelity of [Formula: see text], while the experimental process fidelity on hardware is [Formula: see text]. The observed discrepancy is analyzed in the context of device-specific noise sources, including qubit relaxation, dephasing, and state preparation and measurement (SPAM) errors. Our work provides a concrete fidelity benchmark for the Berkeley gate on superconducting hardware and quantify the impact of realistic noise on a non-trivial two-qubit operation, supporting its use in near-term algorithmic and error-correction applications.

Edge computing demands electronics that simultaneously deliver high performance, ultralow power consumption, and dense integration, yet still remains challenging in growing development of artificial intelligence and Internet of Things. Here, we propose an all-two-dimensional low-power and compact transistor with a ferroelectric gate-all-around (Fe-GAA) architecture for energy-efficient 3D-integrated electronics and neuromorphic edge computing. The Fe-GAA field-effect transistors break the Boltzmann limit of traditional CMOS devices, achieving sub-60 mV dec-1 switching (down to 25.3 mV dec-1), a high on/off ratio (108), and field-effect mobility (310 cm2 V-1 s-1). Capitalizing on ferroelectric polarization dynamics in the surrounding CuInP2S6 gate, we further demonstrate its application prospects as compact edge computing devices for binary logic operations and spiking neurons on a unified hardware platform. Monolithic 3D-integrated logic circuits (inverter and NOR gate) reduce footprint by 50% versus planar CMOS, and the artificial neuron emulates leaky integrate-and-fire (LIF) behavior without additional capacitors and external reset circuitry in a conventional neuron, significantly reducing the energy consumption and hardware footprint. Implemented in a spiking neural network (SNN) for gesture recognition, this system attains 92.71% accuracy on the DVS128Gesture data set. This work establishes a paradigm for multifunctional edge intelligence processors that transcend traditional power-area trade-offs.

This study evaluates the cellular dosimetry of Antimony-119 (119Sb) as a candidate for targeted radionuclide therapy (TRT), using Monte Carlo simulations with the GATE toolkit. The goal is to assess the microdosimetric characteristics of 119Sb as an Auger and Internal conversion electron emitter and compare its performance with other clinically relevant radionuclides-177Lu, 125I, 123I, and 103Pd-across different cellular geometries and emission spectra. Monte Carlo simulations were performed using the GATE toolkit to model energy deposition at the cellular level. Two cell geometries were considered: one with a centrally located nucleus and another with the nucleus adjacent to the cell membrane, reflecting real biological diversity. S-values (absorbed dose per decay) were calculated for three possible locations of radioactivity (nucleus, cytoplasm, and cell membrane). Three emission spectra and two physics models were used to ensure robust results. Simulations were benchmarked against established dosimetry models (MIRDcell, PENELOPE, and Geant4). 119Sb delivered the highest and most localized dose to the cell nucleus, particularly when radioactivity was at the cell membrane, outperforming the other radionuclides tested. Its S-values were up to five times higher than 177Lu and four times higher than 125I in certain configurations. This effect remained strong even when the nucleus was off-center, suggesting 119Sb's effectiveness is independent to nucleus locations diversities. Auger and Coster-Kronig electrons dominated the energy deposition for most isotopes in single cell configurations, while internal conversion electrons contributed significantly for 119Sb in the cytoplasmic and membrane configurations. 119Sb is a promising candidate for TRT of micrometastases and single cancer cells due to its high and stable S-values, especially when radioactivity is localized at the cell membrane. Its dosimetric stability across nucleus positions makes it particularly suitable for therapeutic applications where cellular morphology varies. However, production and radiolabeling challenges may limit its clinical adoption.

Neuromorphic visual perception systems necessitate devices that integrate broadband spectral sensitivity with dynamic adaptation capabilities. However, current optoelectronic synapses often suffer from limited spectral coverage and insufficient electrically programmable adaptation functionalities. To overcome these limitations, we demonstrate a gate-tunable optoelectronic synapse utilizing a two-dimensional (2D) MoS2/GeSe van der Waals heterojunction. By leveraging the complementary absorption profiles of MoS2 and GeSe alongside the synergistic effects of the heterojunction, the device achieves a continuous broadband response ranging from the ultraviolet (295 nm) to the near-infrared (830 nm) regions. Furthermore, the device emulates diverse synaptic plasticities, replicating biological memory dynamics and emulating scotopic and photopic visual adaptation through gate-voltage modulation. Integration of the device into a neuromorphic visual system facilitates efficient image preprocessing and robust recognition. This work presents a potential strategy for developing high-performance, adaptive optoelectronic synapses for intelligent visual perception systems.

This study aimed to validate the dosimetric parameters of the 192Ir GammaMed Plus source used in high-dose-rate (HDR) brachytherapy, using the Monte Carlo simulation code GATE (version 9.3), in accordance with the American Association of Physics in Medicine (AAPM) TG-43 protocol recommendations. The source was modelled inside a 40 cm diameter water-filled spherical phantom to accurately replicate clinical conditions. The dose rate constant, radial dose function g(r), anisotropy function [Formula: see text], and the two-dimensional dose rate distribution matrix were calculated using the DoseActors modules. The results showed excellent agreement with reference data: the dose rate constant was [Formula: see text], with a maximum difference from values reported in the literature of 0.76%, and average deviations of 1.2% and 1.4% for g(r) and [Formula: see text], respectively. These findings suggest that GATE provides reliable and accurate dosimetric estimations and may be a useful tool for dosimetric validation in research settings, particularly in heterogeneous media.

Segmentation of brain tumors using magnetic resonance (MR) images is essential for early diagnosis and effective treatment planning, particularly in areas where tumor subregions exhibit diverse intensity patterns and overlapping boundaries between tumor and normal tissues. Existing methods mainly emphasize global and local contextual tumor representation, often overlooking critical features like structural boundary details and inter feature learning. A novel model that combines complementary feature learning with cross-gated fusion. Complementary learning focuses on both contextual and structural information through two parallel processes: contextual semantic learning and fine edge learning. These learnings extract global semantic context and local edge-aware details. A cross-gated fusion mechanism is introduced in the skip connections to effectively combine these complementary features, adaptively balancing semantic richness with structural precision. A graphical user interface is also developed to facilitate subregion-aware prediction and metric evaluation. On the BraTS2020 dataset, the proposed model achieved mean dice coefficient (DC) of 0.9489, 0.8581, and 0.8352 in whole tumor (WT), tumor core (TC), and enhanced tumor (ET) regions, respectively, and mean intersection over union (IOU) of 0.8961, 0.8572, and 0.8093 in WT, TC and ET regions. The model achieved DC of 0.9365 and IOU of 0.8893 on the LGG-MRI dataset. In the comparative analysis, DC improved by 3.3%, 5.7%, and 3.3% on BraTS2020, BraTS2021, and LGG-MRI datasets respectively, demonstrating the robustness and adaptability of the model. The proposed framework addresses the challenges across multi-class and single-tumor regions and facilitates practical subregion-aware clinical deployment.

Multiphotochromic systems promise multistate molecular information storage, yet strategies to control their switching-state distributions and interchromophore communication remain scarce. Here, we demonstrate that silicon valency can serve as a molecular control element to gate multiphotochromism. Two silicon-based diarylethene Lewis acids undergo stepwise twofold cyclization, with interconversion between tetra- and pentacoordinate states modulating electronic coupling between ligands. Donor coordination enables selective monocyclization, wavelength-gated activation, or complete suppression of photoreactivity, depending on the bound base. In the donor-free state, stepwise cyclization progressively enhances Lewis acidity and culminates in the first example of photoswitchable Lewis superacidity. In addition, dynamic Si─N/Si─O bond metathesis provides thermodynamic access to switching-state distributions beyond statistical photochemical limits. These findings establish valency control as a design principle for multiphotochromic architectures.

Magnetoresistance in the 2D ferromagnetic van der Waals (vdW) Fe3GaTe2 (FGT) has emerged as a new frontier in spintronics, with particular interest paid to the antisymmetric magnetoresistance (AsMR) effect due to its potential to realize multi-state memory, promising for constructing energy-efficient memory devices. However, the mechanism underlying the room-temperature AsMR remains unresolved. Herein, a structural design of two completely isolated FGT nanoflakes, combined with measurement approaches using swapping electrodes and flipping device orientations, was used to clarify the physics of room-temperature AsMR in vdW ferromagnetic FGT-based systems. The results show the unambiguous presence of room-temperature AsMR with four distinct resistance states in the FGT/Pt Hall bar devices. Spin-momentum locking is identified in the vdW FGT-based heterostructures and found to be responsible for the observed room-temperature AsMR. The special design and magneto-electric transport measurements rule out the magnetic domain wall-induced circulating currents and interface pinning as possible origins of AsMR. Further confirmation of this physical mechanism is provided by the distinctive configuration of two FGT nanoflakes separated by a micrometer-scale gap. Overall, the physical mechanism of room-temperature AsMR in vdW FGT/Pt heterostructures is experimentally confirmed, opening new avenues for low-power room-temperature spintronic devices.

In the budding yeast Saccharomyces cerevisiae, adaptation to hyperosmotic stress is mediated by the Hog1 mitogen-activated protein kinase (MAPK) via the high-osmolarity glycerol (HOG) pathway, which comprises a MAPK cascade and two upstream branches, SHO1 and SLN1. In the SHO1 branch, hyperosmotic stress is detected by transmembrane proteins such as Sho1, Opy2, Hkr1, and Msb2; however, the signaling steps directly controlled by the stress have remained unclear. Here, we show that hyperosmotic stress regulates three distinct steps within the SHO1 branch. It promotes the phosphorylation of the MAP2K Pbs2 by the MAP3K Ste11 through Sho1-dependent protein interactions and requires Hkr1, and it also regulates an upstream step required for Ste11 activation that depends on Hkr1 and Opy2. Together with a previously described step in Pbs2-mediated Hog1 phosphorylation, these findings show that osmotic stress regulates the pathway at three levels, defining stepwise multi-gate control of HOG pathway activation.

The opioid crisis has emphasized the need for more effective treatments for opioid use disorder (OUD) 1-3 , which is characterized by habitual opioid use to avoid withdrawal symptoms 4,5 . Both physical and affective symptoms contribute to opioid withdrawal yet whether different neural mechanisms mediate these different symptom domains and contribute distinctly to opioid relapse is unknown. While neurons expressing mu opioid receptors (MORs) gate opioids' reinforcing effects 6-8 by increasing dopamine (DA) release in nucleus accumbens (NAc), sharp decreases in NAc DA release are associated with withdrawal 9-11 , the cellular and circuit mechanisms of which are unknown. Here we describe an unusual population of evolutionarily-conserved MOR+ neurons in the NAc expressing the transcription factor Tshz1 . Increased activity in these neurons is required for withdrawal aversion learning. Deletion of MORs in Tshz1 neurons prevented withdrawal-induced decreases in DA release and affective aversion, but not physical symptoms associated with withdrawal. Pharmacological activation of mGluR8, which is preferentially expressed in Tshz1 neurons, reduced withdrawal aversion. Thus, by dissociating the circuit mechanisms contributing to the physical and affective components of opioid withdrawal focusing on the critical role of Tshz1 neurons, we have identified a novel druggable target with therapeutic potential for treating key OUD withdrawal symptoms.

Electrochemical conversion of nitrate waste into ammonia represents a promising route for closing the anthropogenic nitrogen cycle, yet its efficiency is severely hampered by the sluggish supply of dilute nitrate and the parasitic hydrogen evolution derived from overwhelming interfacial water. Herein, we exploit the potential-driven structural transformation of amphiphilic surfactant assembly from disordered gauche conformations to ordered all-trans conformations to construct a dynamic permselective gate at the reaction interface. In situ spectroscopic investigations reveal that this field-induced reorientation enables the cationic headgroups adjoining the active surface to proactively enrich nitrate species via electrostatic attraction, while the distal long hydrophobic alkyl chains form a densely packed barrier that effectively shields the electrode surface from bulk water to suppress hydrogen evolution. With this tailored microenvironment, the enriched nitrate concentration and regulated proton supply work in synergy to match the reactant availability with the proton-electron transfer, significantly accelerating the reaction kinetics. As a result, the optimized system achieves a remarkable ammonia yield of 295.78 mg h-1 mg-1 and a near-unity Faradaic efficiency of 99.61%. Furthermore, an aqueous Zn-NO3- battery assembled with this cathode delivers a high power density of 37.42 mW cm-2 and simultaneous ammonia production, providing a promising paradigm for energy-efficient nitrogen looping.

Negative magnetoresistance in conventional two-dimensional electron gases is a well known phenomenon, but its origin in complex and topological materials endowed with nontrivial quantum geometry remains elusive. Here, we report a giant negative magnetoresistance reaching  -90% of the zero-field resistance, R0, in n-type tellurene films. The effect persists up to 35 T at cryogenic temperatures and is suppressed when the chemical potential moves away from the conduction-band Weyl node, suggesting a quantum geometric origin. We propose two mechanisms: quantum geometric enhancement of diffusion and a magnetoelectric spin interaction that locks the spin of a cyclotron-moving Weyl fermion, in the presence of an intrinsic inversion-breaking polar field E and an applied magnetic field B, to its guiding-center drift, (E×B)⋅σ. The resulting diffusion enhancement yields ΔRzz/R0=-βg(E×B)2, with βg set by the quantum metric. Our findings establish a quantum geometric, non-Markovian memory effect in magnetotransport.

Sophorolipid, a biosurfactant which can be fermentatively produced by the yeast Starmerella bombicola using sugars and lipids as carbon sources, exhibits several advantageous properties over synthetic detergents, such as biodegradability and low toxicity. While some life cycle assessment (LCA) studies have been reported to evaluate the environmental impact of sophorolipid production, carbon footprint estimations remain limited. In this study, we combined small-scale experimental data with large-scale simulation data to estimate the carbon footprint of the fermentative production of sophorolipid by S. bombicola. Our LCA analysis estimated that sophorolipid production in a 1-L bench-top bioreactor emits 685 kg CO2 eq per kg of product. A scale-up simulation of the carbon footprint resulted in a marked decrease in CO2 emission from sophorolipid production in larger scales. Through sensitivity analysis, we identified key hotspots in the production process, particularly related to production scale.

Androgen deprivation is assumed to boost antitumor immunity-but Lee et al. overturn this logic in glioblastoma, showing that androgen loss activates a microglial inflammasome-hypothalamic-pituitary-adrenal axis cascade that elevates glucocorticoids and attenuates T cell function. The study reveals how organ context reverses endocrine control of tumor immunity.