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HighlightsThe partial removal of ammonium cations from ammonium vanadate results in an expanded interplanar space.The deficient ammonium vanadate exhibits highly reversible redox reaction.Ex situ characterizations suggest the reversible Zn3V2O7(OH)2·2H2O formation/decomposition in deficient ammonium vanadate during charge/discharge processes.Ammonium vanadate with bronze structure (NH4V4O10) is a promising cathode material for zinc-ion batteries due to its high specific capacity and low cost. However, the extraction of NH4+ at a high voltage during charge/discharge processes leads to irreversible reaction and structure degradation. In this work, partial NH4+ ions were pre-removed from NH4V4O10 through heat treatment; NH4V4O10 nanosheets were directly grown on carbon cloth through hydrothermal method. Deficient NH4V4O10 (denoted as NVO), with enlarged interlayer spacing, facilitated fast zinc ions transport and high storage capacity and ensured the highly reversible electrochemical reaction and the good stability of layered structure. The NVO nanosheets delivered a high specific capacity of 457 mAh g−1 at a current density of 100 mA g−1 and a capacity retention of 81% over 1000 cycles at 2 A g−1. The initial Coulombic efficiency of NVO could reach up to 97% compared to 85% of NH4V4O10 and maintain almost 100% during cycling, indicating the high reaction reversibility in NVO electrode.

An exhaustive and integrative overview of recent developments in 3D and 4D textiles based on Additive Manufacturing (AM) were provided in order to identify the current state‐of‐the‐art. Despite all scientific progress, AM applied on textiles is a challenging technique and is still at an embryonic stage of research and technological development (R&TD), mainly due to the technological gap between featured prototypes and scalability in manufacturing. Despite its full potential across a range of different applications, such as development of functional filament fibres/wires, 3D printing on textiles, 3D printing completed garments and 4D textiles, needs future developments. Although, AM applied on textiles, enables cost and resource efficiency for small scale production through localised production, shorten supply chain and demand driven manufacture, both customisable and scalable, embracing cost and environmental sustainability. The opportunities and limits of 3D and 4D printing textiles are also discussed. Finally, the conclusion highlights the potential future development and application of the convergence of advanced computational design techniques, product customization, mathematical modelling, simulation, and digital modelling within multifunctional textiles.

Abstract Bitter taste receptors serve as a vital component in the defense system against toxin intake by animals, and the family of genes encoding these receptors has been demonstrated, usually by family size variance, to correlate with dietary preference. However, few systematic studies of specific Tas2R to unveil their functional evolution have been conducted. Here, we surveyed Tas2R16 across all major clades of primates and reported a rare case of a convergent change to increase sensitivity to β-glucopyranosides in human and a New World monkey, the white-faced saki. Combining analyses at multiple levels, we demonstrate that a parallel amino acid substitution (K172N) shared by these two species is responsible for this functional convergence of Tas2R16. Considering the specialized feeding preference of the white-faced saki, the K172N change likely played an important adaptive role in its early evolution to avoid potentially toxic cyanogenic glycosides, as suggested for the human TAS2R16 gene.

A novel hyperbranched polymer with polyacrylamide side chains (HAPAM) was synthesized by aqueous solution polymerization using acrylic acid, acrylamide, 2-acrylamido-2-methyl-1-propanesulfonic acid, hydrophobic monomer of dimethyl octadecyl ammonium chloride, and the homemade skeleton monomer of modified-M2.0 as raw materials and (NH4)2S2O8-NaHSO3 as initiator. The molecular structure, functional groups, and surface morphology of HAPAM were characterized by Fourier transform infrared spectroscopy, nuclear magnetic resonance hydrogen spectroscopy, and scanning electron microscopy. It was found that the performance of HAPAM solution was higher than that of ordinary polyacrylamide solution in terms of thickening ability, shearing resistance, thermal endurance, salt-resistance, resistance-coefficient and residual-resistance-coefficient, ability to reduce interfacial tension between polymer solution and crude oil, and oil-displacement-efficiency. In particular, the enhanced oil recovery of the HAPAM solution was 13.03%, and the improvement of shearing resistance and immunity to chromatographic separation were simultaneously achieved by the HAPAM solution. These results indicate that the successful synthesis of the novel HAPAM opens a promising strategy for developing new high-performance oil-displacing polymers.

There is an urgent need for a convenient and incidental method to assess the bone health status of the population, especially in primary-level hospitals lacking specialized bone density testing equipment. This study aims to investigate the association between multiple vertebral Hounsfield Unit (HU) value clusters and bone mass subtypes using an unsupervised learning approach, providing a practical tool for incidental osteoporosis screening in clinical settings. This retrospective study included subjects who underwent chest CT and quantitative CT (QCT) from January 2023 to December 2024. Vertebral HU values (T7-T12) were measured on chest CT images. Intergroup comparisons (normal, osteopenia, and osteoporosis) in clinical findings and CT values were performed using Pearson χ test and one-way analysis of variance. An unsupervised -means clustering was applied to vertebral CT values across the cohort. The study comprised 455 participants (260 males, 195 females) with a median age of 60 years (interquartile range, 51-67 years), who were classified into three groups: normal bone mass, 253 cases; osteopenia, 152 cases; osteoporosis, 50 cases. Among 455 participants, age inversely correlated with bone mass. Vertebrae HU values (T7-T12) exhibited significant stepwise declines from normal to osteopenia to osteoporosis (OP) groups. The clustering analysis revealed five distinct subtypes: cluster 1 strongly correlated with OP (45 of 72 cases), cluster 4 with osteopenia (107 of 146 cases), and clusters 2, 3, and 5 with normal bone mass (31 of 31 cases; 90 of 107 cases; 97 of 99 cases). Unsupervised clustering of T7-T12 vertebral HU values effectively stratifies bone mass subtypes, offering an efficient, CT-based screening method for skeletal health assessment, especially valuable in resource-limited primary-level hospitals lacking dedicated bone densitometry.

Amino acid decarboxylases convert amino acids into different biogenic amines which regulate diverse biological processes. Therefore, identifying the substrates of amino acid decarboxylases is critical for investigating the function of the decarboxylases, especially for the new genes predicted to be amino acid decarboxylases. In the present work, we have established a simple and efficient method to identify the substrates and enzymatic activity of amino acid decarboxylases based on LC-MS methods. We chose GAD65 and AADC as models to validate our method. GAD65 and AADC were expressed in HEK 293T cells and purified through immunoprecipitation. The purified amino acid decarboxylases were subjected to enzymatic reaction with different substrate mixtures in vitro. LC-MS analysis of the reaction mixture identified depleted or accumulated metabolites, which corresponded to candidate enzyme substrates and products, respectively. Our method successfully identified the substrates and products of known amino acid decarboxylases. In summary, our method can efficiently identify the substrates and products of amino acid decarboxylases, which will facilitate future amino acid decarboxylase studies.

A better base editor with fewer off-target changes, from a die-hard Manchester United fan.

Pedagogical content knowledge (PCK) has been a cornerstone of science teacher education research, yet its practical application remains limited because of the non-standardized, time-intensive, and labor-intensive nature of PCK data collection and analysis. This study explores the potential of large language models (LLMs) to identify science teachers’ PCK levels on the topic of photosynthesis using open-ended responses and lesson plans. Iterative cycles of training and testing LLMs to assess various PCK components were conducted, introducing an innovative approach that utilized synthetic responses to train the models, which were subsequently validated with actual teacher responses. Findings indicate that synthetic data effectively trained LLMs to identify teacher PCK levels, though performance varied across PCK components. For instance, some models demonstrated strong performance in assessing Knowledge of Instructional Strategies and Representations, as well as Knowledge of Assessment of Science Learning, but struggled with Knowledge of Student Understanding. The study also examined the relationships between teacher characteristics (e.g. self-efficacy, years of experience, and National Board Certification) and PCK levels identified by both humans and LLMs. Results showed some alignment in correlations for particular PCK components, though consistency varied across models. Furthermore, the human-machine reliability for identifying PCK levels from lesson plans approached human-human reliability, with some values exceeding 0.80. These findings highlight the significant potential of LLMs have to advance and scale science teacher PCK research by incorporating multiple data sources. Challenges and opportunities associated with identifying PCK levels using LLMs are discussed, providing implications for future research and science teacher education.

Accurately characterizing rainfall runoff processes in plain farmland, especially at the plot scale with significant micro-topographic features, has presented challenges. Integrated surface–subsurface flow models with high-precision surface flow modules are appropriate tools, yet open-source versions are rare. To address this gap, we proposed an open-source integrated surface–subsurface flow model called the FullSWOF-Plain model, in which the one-dimensional subsurface module Hydrus-1D was integrated with a modified two-dimensional surface water flow module (FullSWOF-2D) using the sequential head method. Various experimental scenarios were simulated to validate the model’s performance, including two outflow cases (i.e., 1D and 2D) without infiltration, a classical one-dimensional infiltration case, and two typical rainfall events at the experimental field. The results demonstrate the accuracy of this proposed model, with the Nash–Sutcliffe efficiency (NSE) of the simulated discharge exceeding 0.90 in the experimental field case and the root mean squared error (RMSE) values for soil moisture at five depths consistently below 0.03 cm3/cm3. However, we observed a lag in the simulated response time of soil moisture due to the neglect of preferential flow. The micro-topography significantly influenced ponding time and ponding areas. Lower local terrain normally experienced earlier surface ponding. Scattered surface ponding water first occurred in the ditch and followed in the relatively low areas in the main field. The concentration process of surface runoff exhibited hierarchical characteristics, with the drainage ditch contributing the most discharge initially, followed by the connection of scattered puddles in the main field, draining excess surface water to the ditch through rills. This quantitative study sheds light on the impact of micro-topography on surface runoff in plain farmland areas.

One of the important tasks in sand liquefaction assessment is to evaluate the likelihood of soil liquefaction. However, most liquefaction assessment methods are deterministic for influencing factors and fail to calculate the liquefaction probability by systematically considering the probability distributions of soil factors. Based on field liquefaction investigation cases, probability distribution fitting and a hypothesis test were carried out. For the variables that failed to pass the fitting and test, the kernel density estimation was conducted. Methods for calculating the liquefaction probability using a Monte Carlo simulation with the probability distribution were then proposed. The results indicated that for (N1)60, SM, S, and GM followed a Gaussian distribution, while CL and ML followed a lognormal distribution; for FC, SM and GM followed a lognormal distribution; and for d50, ML and S followed a Gaussian and lognormal distribution, respectively. The other factors’ distribution curves can be calculated by kernel density estimation. It is feasible to calculate the liquefaction probability based on a Monte Carlo simulation of the variable distribution. The result of the liquefaction probability calculation in this case was similar to that of the existing probability model and was consistent with actual observations. Regional sample differences were considered by introducing the normal distribution error term, and the liquefaction probability accuracy could be improved to a certain extent. The liquefaction probability at a specific seismic level or the total probability within a certain period in the future can be calculated with the method proposed in this paper. It provides a data-driven basis for realistically estimating the likelihood of soil liquefaction under seismic loading and contributes to site classification, liquefaction potential zoning, and ground improvements in seismic design decisions. The practical value of seismic hazard mapping and performance-based design in earthquake-prone regions was also demonstrated.