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Physics-informed neural networks (PINNs) have emerged as a fundamental approach within deep learning for the resolution of partial differential equations (PDEs). Nevertheless, conventional multilayer perceptrons (MLPs) are characterized by a lack of interpretability and encounter the spectral bias problem, which diminishes their accuracy and interpretability when used as an approximation function within the diverse forms of PINNs. Moreover, these methods are susceptible to the over-inflation of penalty factors during optimization, potentially leading to pathological optimization with an imbalance between various constraints. In this study, we are inspired by the Kolmogorov-Arnold network (KAN) to address mathematical physics problems and introduce a hybrid encoder-decoder model to tackle these challenges, termed AL-PKAN. Specifically, the proposed model initially encodes the interdependencies of input sequences into a high-dimensional latent space through the gated recurrent unit (GRU) module. Subsequently, the KAN module is employed to disintegrate the multivariate function within the latent space into a set of trainable univariate activation functions, formulated as linear combinations of B-spline functions for the purpose of spline interpolation of the estimated function. Furthermore, we formulate an augmented Lagrangian function to redefine the loss function of the proposed model, which incorporates initial and boundary conditions into the Lagrangian multiplier terms, rendering the penalty factors and Lagrangian multipliers as learnable parameters that facilitate the dynamic modulation of the balance among various constraint terms. Ultimately, the proposed model exhibits remarkable accuracy and generalizability in a series of benchmark experiments, thereby highlighting the promising capabilities and application horizons of KAN within PINNs.

The demand for flexible and wearable electronic devices with excellent stretchability and sensitivity is increasing, especially for human motion detection. In this work, a simple, low-cost and convenient strategy has been employed to fabricate flexible strain sensor with a composite of carbon black and silver nanoparticles as sensing materials and thermoplastic polyurethane as matrix. The strain sensors thus prepared possesses high stretchability and good sensitivity (gauge factor of 21.12 at 100% tensile strain), excellent static (almost constant resistance variation under 50% strain for 600 s) and dynamic (100 cycles) stability. Compared with bare carbon black-based strain sensor, carbon black/silver nanoparticles composite-based strain sensor shows ~18 times improvement in sensitivity at 100% strain. In addition, we discuss the sensing mechanisms using the disconnection mechanism and tunneling effect which results in high sensitivity of the strain sensor. Due to its good strain-sensing performance, the developed strain sensor is promising in detecting various degrees of human motions such as finger bending, wrist rotation and elbow flexion.

Recent advances in artificial intelligence (AI), blockchain, virtual reality (VR), and digital twin technologies are transforming approaches to cultural heritage conservation. This study develops an integrated analytical framework that combines AI-driven modeling, interactive functionality, and blockchain/NFT authentication to examine both the direct and mediating effects of these technologies on heritage conservation effectiveness (HCE). Digital twins serve as a core component for simulating and managing heritage environments through dynamic, data-driven representations. An empirical analysis using Partial Least Squares Structural Equation Modeling (PLS-SEM) was conducted on 575 valid survey responses. The results indicate that blockchain/NFT authentication indirectly enhances heritage conservation effectiveness by improving digital authenticity. The study theoretically pioneers the integration of multiple digital technologies into a unified framework and empirically demonstrates the mediating roles of user immersive experience and digital authenticity. Practically, the findings offer actionable insights for advancing digital heritage conservation within metaverse environments and intelligent cultural ecosystems.

The role of N-heterocyclic carbene, a well-known reactive site, in chemical catalysis has long been studied. However, its unique binding and electron-donating properties have barely been explored in other research areas, such as metal capture. Herein, we report the design and preparation of a poly(ionic liquid)-derived porous organic polycarbene adsorbent with superior gold-capturing capability. With carbene sites in the porous network as the “nanotrap”, it exhibits an ultrahigh gold recovery capacity of 2.09 g/g. In-depth exploration of a complex metal ion environment in an electronic waste-extraction solution indicates that the polycarbene adsorbent possesses a significant gold recovery efficiency of 99.8%. X-ray photoelectron spectroscopy along with nuclear magnetic resonance spectroscopy reveals that the high performance of the polycarbene adsorbent results from the formation of robust metal-carbene bonds plus the ability to reduce nearby gold ions into nanoparticles. Density functional theory calculations indicate that energetically favourable multinuclear Au binding enhances adsorption as clusters. Life cycle assessment and cost analysis indicate that the synthesis of polycarbene adsorbents has potential for application in industrial-scale productions. These results reveal the potential to apply carbene chemistry to materials science and highlight porous organic polycarbene as a promising new material for precious metal recovery. Efficient and selective gold recovery from electronic waste is highly demanded. Here, authors demonstrate the application of a porous organic polycarbene adsorbent with up to 2.09 g/g gold-capturing capability.

The ability to grow properly sized and good quality crystals is one of the cornerstones of single-crystal diffraction, is advantageous in many industrial-scale chemical processes 1 – 3 , and is important for obtaining institutional approvals of new drugs for which high-quality crystallographic data are required 4 – 7 . Typically, single crystals suitable for such processes and analyses are grown for hours to days during which any mechanical disturbances—believed to be detrimental to the process—are carefully avoided. In particular, stirring and shear flows are known to cause secondary nucleation, which decreases the final size of the crystals (though shear can also increase their quantity 8 – 14 ). Here we demonstrate that in the presence of polymers (preferably, polyionic liquids), crystals of various types grow in common solvents, at constant temperature, much bigger and much faster when stirred, rather than kept still. This conclusion is based on the study of approximately 20 diverse organic molecules, inorganic salts, metal–organic complexes, and even some proteins. On typical timescales of a few to tens of minutes, these molecules grow into regularly faceted crystals that are always larger (with longest linear dimension about 16 times larger) than those obtained in control experiments of the same duration but without stirring or without polymers. We attribute this enhancement to two synergistic effects. First, under shear, the polymers and their aggregates disentangle, compete for solvent molecules and thus effectively ‘salt out’ (that is, induce precipitation by decreasing solubility of) the crystallizing species. Second, the local shear rate is dependent on particle size, ultimately promoting the growth of larger crystals (but not via surface-energy effects as in classical Ostwald ripening). This closed-system, constant-temperature crystallization driven by shear could be a valuable addition to the repertoire of crystal growth techniques, enabling accelerated growth of crystals required by the materials and pharmaceutical industries. A method of growing crystals that does not require undisturbed solutions involves adding polyelectrolytes to the starter solution and shearing (that is, stirring).

Objective To evaluate the prognostic value of three-dimensional echocardiography-derived left atrioventricular coupling index (LACI) in patients with chronic kidney disease (CKD) and concomitant heart failure with preserved ejection fraction (HFpEF). Methods An analysis of 108 patients with CKD combined with HFpEF was conducted. Participants were categorized into three groups based on LACI, and differences in clinical and echocardiographic characteristics between groups were assessed. Independent associations between LACI and clinical outcomes were assessed using Cox proportional hazards regression modeling. In addition, an analysis was performed to determine the predictive performance of LACI for adverse cardiovascular events by calculating the area under the curve (AUC) and the optimal threshold. Results The median LACI in this study was 0.25. When stratified by tertiles, patients in the high LACI group (> 0.33) were significantly older, had more advanced CKD stages, and had more severe left ventricular diastolic dysfunction (LVDD) (all P  < 0.05). With increasing LACI levels, the proportion of grade 1 LVDD progressively decreased while grade 3 LVDD increased (7%, 21%, 58%; P  < 0.001). An LACI ≥ 0.235 demonstrated discriminative capability for moderate-to-severe LVDD (AUC = 0.739, P  < 0.001). Multivariable Cox proportional hazards regression confirmed LACI as an independent predictor of major clinical outcomes (HR = 1.32, 95% CI: 0.792–0.894, P  < 0.001). The optimal cutoff value of 0.26 (AUC = 0.843, P  < 0.001) effectively stratified high-risk patients (log-rank P  < 0.001). Net reclassification improvement (NRI) analysis demonstrated that incorporating LACI significantly enhanced the incremental predictive value of traditional clinical models. Conclusion LACI, quantified by three-dimensional echocardiography (3DE), correlates with the severity of LVDD. LACI is an independent predictor of adverse clinical outcomes in patients with CKD combined with HFpEF, providing an objective quantitative metric that can guide therapeutic decision-making in this high-risk population.

Abnormalities in lipid metabolism play an important role in diabetic macular edema (DME), and the aim of this study was to investigate the correlation between ApoB/A1 levels and best corrected visual acuity (BCVA) and macular microstructural changes in DME patients after anti-VEGF treatment. Through a retrospective cohort analysis of 61 patients (61 eyes) with non-proliferative diabetic retinopathy combined with macular edema treated with 3 + PRN anti-VEGF regimen and followed up for three months, grouped by median ApoB/A1, the differences between the efficacy indexes of the two groups were compared. The results showed that the macular edema regression rate was significantly higher in the high ApoB/A1 ratio group than in the low ratio group at one month after treatment( P  < 0.05), and at three months after treatment, the high ApoB/A1 ratio group was better than the low ratio group in BCVA improvement (58.1% vs. 26.7%), inner retinal layer restoration (38.7% vs. 10.0%), and hyperreflective foci (HF) reduction (41.9% vs. 6.7%). aspects were better than those in the low ratio group ( P  < 0.05). The results of ordered logistic regression analysis showed that the ApoB/A1 ratio was significantly correlated with the change in macular edema at one month after treatment and the change in the number of HF at three months after treatment. Conclusions showed that the ApoB/A1 ratio was significantly correlated with short-term improvement of BCVA and macular microstructure after anti-VEGF treatment in DME patients, and it is expected to be used as an objective biomarker for assessing the efficacy of anti-VEGF treatment in DME patients.

Wild teas are valuable genetic resources for studying domestication and breeding. Here we report the assembly of a high-quality chromosome-scale reference genome for an ancient tea tree. The further RNA sequencing of 217 diverse tea accessions clarifies the pedigree of tea cultivars and reveals key contributors in the breeding of Chinese tea. Candidate genes associated with flavonoid biosynthesis are identified by genome-wide association study. Specifically, diverse allelic function of CsANR , CsF3’5’H and CsMYB5 is verified by transient overexpression and enzymatic assays, providing comprehensive insights into the biosynthesis of catechins, the most important bioactive compounds in tea plants. The inconspicuous differentiation between ancient trees and cultivars at both genetic and metabolic levels implies that tea may not have undergone long-term artificial directional selection in terms of flavor-related metabolites. These genomic resources provide evolutionary insight into tea plants and lay the foundation for better understanding the biosynthesis of beneficial natural compounds. Wild teas are considered as valuable resource for studying domestication and breeding. Here, Zhang et al. report genome of wild tea DASZ and transcriptome of 217 accessions, which clarify pedigree of Chinese tea cultivars and show tea may not have undergone long-term artificial directional selection on flavor-related metabolites.

Chronic pain remains to be a clinical challenge and is recognized as a major health problem with varying impacts on quality of life. Currently, the first-line therapy for chronic pain is opioids, which are often accompanied by unwanted psychoactive side effects. Thus, new and effective treatments for chronic pain are urgently needed and eagerly pursued. Inflammatory cytokines, especially interleukin-17 (IL-17), are reportedly potential therapeutic targets owing to their pivotal role in chronic pain from the neuroinflammation perspective. Recently, substantial evidence confirmed that IL-17 and IL-17 receptors (IL-17Rs) were increased in neuropathic, inflammatory, and cancer pain models. Notably, IL-17/IL-17R antibodies also reportedly relieve or cure inflammatory- and pain-related diseases. However, existing studies have reported controversial results regarding IL-17/IL-17Rs as potential therapeutic targets in diverse animal models of chronic pain. In this review, we present a summary of published studies and discuss the evidence, from basic to clinical to research, regarding the role and mechanism of action between IL-17 and diverse kinds of chronic pain in animal models and clinical patients. Furthermore, we evaluated IL-17-based therapy as a potential therapeutic strategy for inflammatory- and pain-related disease. Importantly, we also discussed clinical trials of IL-17/IL-17R targeting monoclonal antibodies. Overall, we found that IL-17 is a potential therapeutic target for chronic pain from the perspective of neuroinflammation.

Lipid droplets (LDs) are dynamic lipid storage organelles that can be degraded by autophagy machinery to release neutral lipids, a process called lipophagy. However, specific receptors and regulation mechanisms for lipophagy remain largely unknown. Here, we identify that ATG14, the core unit of the PI3KC3-C1 complex, also targets LD and acts as an autophagic receptor that facilitates LD degradation. A negative regulator, Syntaxin18 (STX18) binds ATG14, disrupting the ATG14-ATG8 family members interactions and subverting the PI3KC3-C1 complex formation. Knockdown of STX18 activates lipophagy dependent on ATG14 not only as the core unit of PI3KC3-C1 complex but also as the autophagic receptor, resulting in the degradation of LD-associated anti-viral protein Viperin. Furthermore, coronavirus M protein binds STX18 and subverts the STX18-ATG14 interaction to induce lipophagy and degrade Viperin, facilitating virus production. Altogether, our data provide a previously undescribed mechanism for additional roles of ATG14 in lipid metabolism and virus production. Lipophagy is the degradation of lipid droplets by the autophagy machinery. Here, the authors identify that autophagy protein ATG14 also targets lipid droplets and interacts with ATG8 proteins, functioning as an autophagic receptor for STX18-regulated lipophagy.