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Most foldable protein sequences adopt only a single native fold. Recent protein design studies have, however, created protein sequences which fold into different structures apon changes of environment, or single point mutation, the best characterized example being the switch between the folds of the GA and GB binding domains of streptococcal protein G. To obtain further insight into the design of sequences which can switch folds, we have used a computational model for the fitness landscape of a single fold, built from the observed sequence variation of protein homologues. We have recently shown that such coevolutionary models can be used to design novel foldable sequences. By appropriately combining two of these models to describe the joint fitness landscape of GA and GB, we are able to describe the propensity of a given sequence for each of the two folds. We have successfully tested the combined model against the known series of designed GA/GB hybrids. Using Monte Carlo simulations on this landscape, we are able to identify pathways of mutations connecting the two folds. In the absence of a requirement for domain stability, the most frequent paths go via sequences in which neither domain is stably folded, reminiscent of the propensity for certain intrinsically disordered proteins to fold into different structures according to context. Even if the folded state is required to be stable, we find that there is nonetheless still a wide range of sequences which are close to the transition region and therefore likely fold switches, consistent with recent estimates that fold switching may be more widespread than had been thought.

The surface structure of heterogeneous catalysts is closely associated with their catalytic performance. Current efforts for structural modification mainly focus on improving the catalyst synthesis details. Here we reveal an induced activation strategy to manipulate the catalyst surface reconstruction process by controlling the composition of reducing agents at the activation stage. Exposing the commercial Cu/ZnO/Al 2 O 3 catalyst to a H 2 /H 2 O/CH 3 OH/N 2 mixture at 300 °C and atmospheric pressure is found to accelerate the migration of ZnO x species onto the surface of metallic Cu 0 nanoparticles via an adsorbate-induced strong metal–support interaction. Such a morphological evolution improves the long-term stability by threefold and results in more abundant Cu–ZnO x interfacial sites with catalytic activity enhanced by twofold towards the methanol steam reforming reaction. The encapsulation of supported metal nanoparticles by a thin oxide support overlayer under reducing conditions is commonly referred to as a strong metal–support interaction. Now, by controlling the composition of the reducing agents at the catalyst activation stage, an adsorbate-induced strong metal–support interaction is reported for the commercial Cu/ZnO/Al 2 O 3 catalyst during methanol steam reforming.

Sulfate aerosols exert profound impacts on human and ecosystem health, weather, and climate, but their formation mechanism remains uncertain. Atmospheric models consistently underpredict sulfate levels under diverse environmental conditions. From atmospheric measurements in two Chinese megacities and complementary laboratory experiments, we show that the aqueous oxidation of SO₂ by NO₂ is key to efficient sulfate formation but is only feasible under two atmospheric conditions: on fine aerosols with high relative humidity and NH₃ neutralization or under cloud conditions. Under polluted environments, this SO₂ oxidation process leads to large sulfate production rates and promotes formation of nitrate and organic matter on aqueous particles, exacerbating severe haze development. Effective haze mitigation is achievable by intervening in the sulfate formation process with enforced NH₃ and NO₂ control measures. In addition to explaining the polluted episodes currently occurring in China and during the 1952 London Fog, this sulfate production mechanism is widespread, and our results suggest a way to tackle this growing problem in China and much of the developing world.

Accurately determining responsibility in traffic accidents is crucial for ensuring fairness in law enforcement and optimizing responsibility standards. Traditional methods predominantly rely on subjective judgments, such as eyewitness testimonies and police investigations, which can introduce biases and lack objectivity. To address these limitations, we propose the AMFormer(Arithmetic Feature Interaction Transformer) framework—a deep learning model designed for robust and interpretable traffic accident responsibility prediction. By capturing complex interactions among key factors through spatiotemporal feature modeling, this framework facilitates precise multi-label classification of accident responsibility. Furthermore, we employ SHAP (SHapley Additive Interpretation) analysis to improve transparency by identifying the most influential features in attribution of responsibility, and provide an in-depth analysis of key features and how they combine to significantly influence attribution of responsibility. Experiments conducted on real-world datasets demonstrate that AMFormer outperforms both other deep learning models and traditional approaches, achieving an accuracy of 93.46% and an F1-Score of 93%. This framework not only enhances the credibility of traffic accident responsibility attribution but also establishes a foundation for future research into autonomous vehicle responsibility.

The E. coli ribosome exit tunnel can accommodate small folded proteins, while larger ones fold outside. It remains unclear, however, to what extent the geometry of the tunnel influences protein folding. Here, using E. coli ribosomes with deletions in loops in proteins uL23 and uL24 that protrude into the tunnel, we investigate how tunnel geometry determines where proteins of different sizes fold. We find that a 29-residue zinc-finger domain normally folding close to the uL23 loop folds deeper in the tunnel in uL23 Δloop ribosomes, while two ~ 100 residue proteins normally folding close to the uL24 loop near the tunnel exit port fold at deeper locations in uL24 Δloop ribosomes, in good agreement with results obtained by coarse-grained molecular dynamics simulations. This supports the idea that cotranslational folding commences once a protein domain reaches a location in the exit tunnel where there is sufficient space to house the folded structure.

This paper presents a novel deep learning-based multi-parameter coupling compensation algorithm for clamp-on gas metering systems to address the complex interdependencies between temperature, pressure, and density variations that significantly affect measurement accuracy. Traditional linear and polynomial compensation methods fail to capture the nonlinear coupling effects between environmental parameters, leading to substantial measurement errors under dynamic operating conditions. The proposed approach employs a hybrid LSTM-CNN neural network architecture that simultaneously models temporal dependencies and spatial relationships within the multi-parameter space, enabling more accurate compensation compared to conventional methods. The algorithm incorporates real-time adaptive correction mechanisms with sliding window processing and dynamic weight adjustment to automatically respond to changing operating conditions. Experimental validation demonstrates significant performance improvements, achieving 0.52% average measurement error compared to 2.45% for conventional linear compensation, representing a 78% accuracy enhancement. Long-term stability testing confirms consistent performance over 720-hour continuous operation with 5.34 millisecond real-time processing capability suitable for industrial implementation. The research contributes to advancing precision gas flow measurement technology by providing a practical solution for achieving high-accuracy measurements under complex industrial operating conditions.

Manipulation of confined water dynamics by voltage keeps great importance for diverse applications. However, limitations on the membrane functions, voltage-control range, and unclear dynamics need to be addressed. Herein, we report an anomalous electrically controlled gating phenomenon on cation-intercalated multi-layer Ti 3 C 2 membranes and reveal the confined water dynamics. The water permeation rate was improved rapidly following the application and rise of voltage and finally reached a maximum rate at 0.9 V. The permeation rate starts to decrease from 0.9 V. Below 0.9 V, the electric field affects the charge and polarity of water molecules and then leads to ordered and denser rearrangement in the two-dimensional (2D) channel to accelerate the permeation rate. Above 0.9 V, with the assistance of metal cations, the surge in current induced aggregation of water molecules into clusters, thereby limiting the water mobility. Based on these findings, a high-performance humidity sensor was developed by simultaneously optimizing the response and recovery speeds through electric manipulation. This work provides flexible strategies in intelligent membrane design and nanofluidic sensing. Nanoconfined water has unique properties, often leading to the discovery of unexpected phenomena, which play key roles in applications such as sensing, filtration, and catalysis. Here authors report electrically controlled gating in cation (K + /Li + ) intercalated multilayer Ti 3 C 2 and describe anomalous water dynamics in electrical angstrom-scale channels.

Cracks are a common form of road distress that can significantly impact pavement integrity. Accurate detection of the attribute characteristics of cracks, including the type, location (top and bottom), width, and orientation, is crucial for effective repair and treatment. This study combines numerical simulations with filed data to investigate how the amplitudes of ground-penetrating radar (GPR) early-time signals (ETSs) vary with changes in the crack top and width, as well as how variations in the crack bottom impact radar reflected wave amplitude. The results show that when GPR ETSs are mixed with diffracted waves from the crack top, the amplitude change percentage of the ETS at the crack top exhibits a pronounced ‘∨’-shaped dip, which provides a clearer indication of the crack top. Furthermore, a positive correlation exists between crack width and the amplitude change percentage, offering a theoretical basis for quantitatively estimating crack width. On the reflected wave originating from the interface between the semi-rigid base and the subgrade, a pronounced ‘∧’-shaped dip is observed in the trough amplitude change percentage of the reflected wave at the crack bottom. For cracks of the same width, the amplitude of the ‘∧’ vertex from reflective cracks is approximately three times greater than that from fatigue cracks. This discrepancy helps identify the crack bottom and quantitatively diagnose their types. The line connecting the vertices of the ‘∨’ and ‘∧’ shapes indicate the crack’s orientation. Accurate diagnosis of crack properties can guide precise, minimally invasive treatment methods, effectively repairing road cracks and extending the road’s service life.

The seasonal and spatial variations of vertical distribution and optical properties of aerosols over China are studied using long-term satellite observations from the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) and ground-based lidar observations and Aerosol Robotic Network (AERONET) data. The CALIOP products are validated using the ground-based lidar measurements at the Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL). The Taklamakan Desert and Tibetan Plateau regions exhibit the highest depolarization and color ratios because of the natural dust origin, whereas the North China Plain, Sichuan Basin and Yangtze River Delta show the lowest depolarization and color ratios because of aerosols from secondary formation of the anthropogenic origin. Certain regions, such as the North China Plain in spring and the Loess Plateau in winter, show intermediate depolarization and color ratios because of mixed dust and anthropogenic aerosols. In the Pearl River Delta region, the depolarization and color ratios are similar to but higher than those of the other polluted regions because of combined anthropogenic and marine aerosols. Long-range transport of dust in the middle and upper troposphere in spring is well captured by the CALIOP observations. The seasonal variations in the aerosol vertical distributions reveal efficient transport of aerosols from the atmospheric boundary layer to the free troposphere because of summertime convective mixing. The aerosol extinction lapse rates in autumn and winter are more positive than those in spring and summer, indicating trapped aerosols within the boundary layer because of stabler meteorological conditions. More than 80 % of the column aerosols are distributed within 1.5 km above the ground in winter, when the aerosol extinction lapse rate exhibits a maximum seasonal average in all study regions except for the Tibetan Plateau. The aerosol extinction lapse rates in the polluted regions are higher than those of the less polluted regions, indicating a stabilized atmosphere due to absorptive aerosols in the polluted regions. Our results reveal that the satellite and ground-based remote-sensing measurements provide the key information on the long-term seasonal and spatial variations in the aerosol vertical distribution and optical properties, regional aerosol types, long-range transport and atmospheric stability, which can be utilized to more precisely assess the direct and indirect aerosol effects on weather and climate.

Metal-free materials have been proved to be promising replacements of traditional metal-based catalysts for advanced oxidation reactions. Carbon nitride was found to be able to activate H2O2 and generate hydroxyl radicals (•OH). Nevertheless, the performance of carbon nitride is highly dependent on an external light source. In this work, we report a light-independent, metal-free catalyst based on g-C3N4 prepared using a facile calcination method. It is revealed that two reaction pathways, a radical (•OH) one and a nonradical (H2O2) one, coexist in organics oxidation on g-C3N4. The dominant reaction pathway is dependent on the condensation temperature of UCN. In addition, this g-C3N4 exhibited excellent stability after being recycled and reused for five cycles. The findings in this work can be used for the design of efficient and robust metal-free catalysts with both superior catalytic performance and high stability for various heterogeneous catalytic processes.