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Data-driven explainable chronic kidney disease detection using RF based data imputation and meta-ensemble learning
Highly Efficient Tandem Electrosynthesis of Dimethyl Carbonate From CO <sub>2</sub>
ABSTRACT Current DMC production is energy‐intensive, while generating CO 2 emissions. Electrosynthesis of DMC from CO 2 offers a sustainable alternative but faces challenges of inert CO 2 activation and unfavorable C‐O coupling, leading to low selectivity and current density. Herein, we propose a tandem electrocatalysis strategy for efficient DMC production from CO 2 under ambient conditions, utilizing a cobalt polyphthalocyanine framework (CoPPc) with single‐atom sites and atomically precise palladium‐sulfur clusters (Pd 6 S 12 ). The CoPPc‐Pd 6 S 12 tandem catalyst system synergistically activates CO 2 and enhances mass transport, elevating the local concentration of key intermediates (CO and methoxy species) for improving DMC production efficiency. Thus, the tandem system delivers exceptional performance with 93.4% DMC selectivity and 34.9 mmol L −1 h −1 production in 0.1 M NaBr methanol electrolyte, while achieving a recorded current density of 155.2 mA cm −2 at 0.5 M electrolyte concentration. In situ generated CO and methoxy over CoPPc efficiently transfer to the Pd‐S site, where achieves intermediate stabilization and optimal coupling orientation, boosting DMC selectivity and current density. Moreover, DFT calculations reveal that the CoPPc‐Pd 6 S 12 system reduces the energy barrier for C‐O coupling, facilitating DMC formation. This work provides an efficient tandem electrocatalysis system for DMC production from CO 2 with high selectivity and large current density.
The interaction between lifestyle and blood pressure on Stroke: A cross-sectional study from Northern China
Objective This study aimed to explore and quantify the extent of interaction between lifestyle factors and systolic and diastolic blood pressure on stroke among adults over 65 years of age. This investigation sought to provide valuable insights into the multifactorial risk factors for stroke, with the ultimate goal of supporting clinicians in implementing more focused and comprehensive preventive strategies. Methods Data were obtained from the 2019 health examination records of community hospitals in northern China. A stratified cluster random sampling method was used to select a representative sample from resident health records within the essential public health service management system. Participants were categorized into four subgroups based on systolic and diastolic blood pressure levels. Odds ratios (ORs) with 95% confidence intervals (CIs) and trend tests were used to examine the association between blood pressure categories and incident stroke. In the lifestyle subgroup analysis, a multiplicative interaction model within binary logistic regression was employed to assess the effect of interactions between unhealthy lifestyles and different blood pressure levels on stroke. Results A total of 34,995 eligible subjects were included in the analysis, comprising 44.3% males (n = 15,484) and 55.7% females (n = 19,511). The age range was 65–103 years, with a mean age of 71.91 ± 5.65 years. After adjusting for confounding factors, systolic and diastolic blood pressure (both as continuous and categorical variables) showed a linear positive correlation with stroke incidence(Ptrend for SBP = 0.001; Ptrend for DBP = 0.001). In the lifestyle subgroup analysis, this positive correlation remained significant across most subgroups; Furthermore, Interaction and joint effect analyses revealed that elevated SBP/DBP and unhealthy lifestyle factors synergistically augmented stroke risk after multivariable adjustment. Conclusions This study demonstrated that elevated systolic and diastolic blood pressure were significantly associated with a higher incidence of stroke. A synergistic effect was observed between unhealthy lifestyle factors (such as smoking, alcohol consumption, obesity, and physical inactivity) and elevated blood pressure in increasing stroke risk. Smoking partially mediated the relationship between diastolic blood pressure and stroke. These findings highlight the potential benefit of integrated control strategies targeting both blood pressure and modifiable lifestyle factors.
Non-ablative transurethral laser treatment for collagen remodeling with functional recovery in an in vivo model of stress urinary incontinence
Encapsulated Non‐Exchangeable Na <sup>+</sup> Ions Determining the Upper Limit of Al Inclusion in FAU—A Multiscale Simulation
ABSTRACT Na + ion as charge‐balance agent controlling the distribution of framework aluminum atoms in Y‐zeolite from 1 to 14 Al atoms (Si/Al = 47‐2.4) and the acid strength have been investigated based on DFT computations, descriptors from machine learning, AIMD simulations, and experimental analysis. Different from previous results with proton (H + ) as charge‐balancing agent preferring next‐nearest‐neighbor Al separation (3N‐Al), the framework Al atoms, in the presence of Na + ions, prefer larger separations (4N‐Al and 5N‐Al) due to the balance between Na + /AlO 4 − attractive and AlO 4 − /AlO 4 − repulsive interaction and the preferential occupancy of Na + ions following the order of six‐membered ring (6MR) > double six‐membered ring (D6R) > four‐membered ring (4MR). The computed sequential substitution enthalpy for 1‐14 Al shows the thermodynamically favorable upper limit of rather low Si/Al ratio (≤ 3) and explains the difficult synthesis of Y‐zeolite with higher Si/Al ratios. Y‐zeolite with non‐exchangeable Na + ions has stronger acid strength based on the adsorption energy of pyridine and ammonia and exhibits higher catalytic activity in propane cracking.
Artificial intelligence–Driven detection and decision support system for precision management of maize downy mildew
Artificial intelligence (AI) enables rapid and precise plant disease detection, offering transformative potential for crop protection. Maize downy mildew (MDM), a destructive disease, causes substantial yield losses, making early detection critical. In this study, we evaluated the performance of thirteen machine-learning (ML) and deep-learning (DL) algorithms for classifying healthy and infected maize leaves using a curated field dataset. Model performance was assessed using multiple metrics, including accuracy, precision, recall, F1-score, and AUC-ROC. Among the tested models, VGG16 achieved the highest performance, with 97% accuracy, 0.98 precision, 0.95 recall, 0.97 F1-score, and an AUC-ROC of 0.99. Training and validation curves indicated minimal overfitting, demonstrating robust generalization. Feature visualization using t-SNE revealed clear separability between healthy and diseased samples, while Grad-CAM analysis confirmed that VGG16 focused on biologically relevant symptomatic regions, such as chlorotic streaks and leaf discoloration. Confusion matrix analysis further validated near-perfect classification, with very few misclassifications. Furthermore, we developed a web-based application ( https://maize-mdm.streamlit.app/ ) that not only classifies MDM but also provides farm-level advisory measures. Two-year field trials of DSS-guided fungicide applications effectively suppressed MDM, reducing disease severity (PDI 3.20–5.20; PROC 93−96%), increasing grain yield (75.6–80.2 q/ha; PIOC 195−289%), and improving economic returns (B:C ratio 3.36–3.57) compared to untreated controls. Overall, this study demonstrates that AI-driven models, integrated with web-based decision support, provide accurate, interpretable, and actionable solutions for precision management of maize diseases, contributing to improved yield, profitability, and sustainable agricultural practices.
Streptomyces koyangensis L-asparaginase: computational prediction of dual-mechanism BCL-2 interaction in acute lymphoblastic leukemia
A General Group Testing Strategy for Discovering Chemical Cooperativity
ABSTRACT The combinatorial explosion inherent to multi‐component systems limits their experimental exploration and ultimately chemical discovery. Here, we introduce a statistics‐based group‐testing strategy, which we couple with luminescence quenching assays to efficiently identify cooperative molecular interactions. Utilizing the quenching of a photosensitizer as a quick readout for chemical activity, 4,950 substrate pairs were screened in only 504 experiments, enabled through a combinatorial design theory‐based pooling approach and iterative deconvolution. Therefore, two algorithms—a greedy algorithm for group design and an iterative sectioning deconvolution method to resolve active pairs—were implemented. Fifteen cooperative pairs were identified, and the nature of their interactions and the resulting electronic perturbations were investigated. In a systematic follow‐up screen, it was found that the identified active pairs exhibit high reactivity towards a broad group of reaction partners. One such pair led to the discovery of a bench‐stable reagent, enabling efficient and regioselective trifluoromethylthiolation reactions. This work establishes a broadly applicable framework for accelerating the discovery of cooperative reactivity through optimized experimental designs.
Thermal imaging for sealing defect detection in pharmaceutical bags using a temporal fusion network
Sealing defects in pharmaceutical plastic bags pose significant risks to drug safety, as micro-leakages may remain undetected until transportation, causing economic losses and hazards. Traditional manual inspection and existing automated methods suffer from low efficiency, poor sensitivity to subtle defects, and difficulties in addressing class imbalance due to scarce defective samples. To address these issues, this study proposes a comprehensive detection framework that integrates thermal imaging analysis, physics-guided data augmentation, and a novel Temporal Multi-Feature Fusion Network (TMFFNet). Thermal imaging reveals defective areas with distinct localized temperature elevations, providing a reliable basis for defect identification. A physics-guided augmentation method is developed to synthesize realistic defects: it models defect contours via hybrid polynomials, simulates thermal diffusion using dual-Gaussian operators, and fuses synthetic defects into normal samples under geometric constraints. This method effectively mitigates class imbalance, expanding the number of defective samples from 28 real ones to 2104 synthetic ones, with a total of 4385 samples in the dataset. The proposed TMFFNet, a dual-branch temporal network, processes three consecutive thermal frames to capture temporal dynamics. Its global-local fusion module enhances sensitivity to small defects, while a channel-aware SE-Dense module suppresses background noise, reducing false alarms. Experimental results show that TMFFNet outperforms traditional networks with a test set accuracy of 0.9809, and other evaluation metrics also demonstrate favorable performance. This framework provides an efficient, non-destructive solution for full pharmaceutical packaging inspection, improving drug safety and production efficiency.
QRGEC: quantum reinforcement learning with golden jackal optimization for resilient edge cloud coordination in internet computing
Abstract The rapid growth of Internet scale computing has exposed critical limitations in existing edge cloud coordination mechanisms, particularly in terms of resilience, energy efficiency, and adaptability under heterogeneous and dynamic environments. Current optimization and learning based approaches often suffer from slow convergence, limited exploration capability, and poor robustness when managing distributed edge cloud resources. To address these challenges, this research proposes QRGEC: Quantum Reinforcement Learning with Golden Jackal Optimization for Resilient Edge Cloud Coordination in Internet Computing. The proposed hybrid framework integrates quantum focused policy exploration with adaptive metaheuristic tuning to enhance distributed Internet computing optimization. Policy representations are encoded using variational quantum circuits, enabling efficient exploration of high dimensional decision spaces. Furthermore, the Golden Jackal Optimization mechanism adaptively adjusts reinforcement parameters to improve convergence stability and accelerate learning, thereby enabling resilient and energy-efficient coordination across heterogeneous edge and cloud environments. A resilience aware scheduler seamlessly balances energy efficiency, latency, and recovery within dynamic edge cloud workloads. Extensive experimental evaluations in QRGEC demonstrate that the framework is capable of outperforming previously established deep reinforcement and quantum heuristic baselines with a latency reduction of 36.8%, an increase in energy efficiency of 24.7%, an improvement in resilience of 48.2%, and a sustained resource utilization of 94%. QRGEC also displays the ability to automatically recover from network congestion and failures, recover from network congestion, and maintain balances latency energy trade-offs. This emphasizes the efficiency of QRGEC in autonomous recovery from network failures, making latency energy balance adjustments, and conserving energy.
Ultra‐Low‐Cost Hydrophobic Organic Coating for Highly Reversible Zinc Anodes
ABSTRACT Electrolyte additive engineering offers a promising pathway for achieving dendrite‐free aqueous zinc‐ion batteries (ZIBs) while facing challenges related to hydrophilic characteristics and/or high loading requirements. Herein, we developed a cost‐effective and scalable facile immersion treatment to deposit a hydrophobic 1,3‐Di(o‐tolyl)thiourea (DTH) layer with nanoscale thickness (≤ 14 nm). This approach yields an ultra‐low DTH loading (5.37 × 10 −13 M) and exceptional cost efficiency (1.43 × 10 −7 USD Ah −1 ), surpassing conventional water‐miscible organic additives and biomass‐derived counterparts by orders of magnitude. The hydrophobic DTH layer optimizes Zn electrochemistry and mitigates parasitic reactions, irrespective of the immersion sequence in the same batch of ethanol solution. Consequently, the Zn||ODASnI 4 (ODA denotes 1,8‐octadiamine) coin cell demonstrated stable operation over 2500 cycles at 2 A g −1 with a low additive cost of 1.43 × 10 −6 USD per cell. The pouch cell showed an average coulombic efficiency (CE) of 99.9% and 72% capacity retention after 1200 cycles, incurring an ultra‐low additive cost of 7.02× 10 −5 USD while delivering a high energy density of 143 Wh kg −1 (based on cathode mass). This work enabled durable and high‐performance ZIBs at minimal cost, providing a foundation for further exploration of low‐cost, scalable strategies in aqueous battery systems.
Correction: Dosage suppressors of gpn2ts mutants and functional insights into the role of Gpn2 in budding yeast
Effective of local morphine, ketorolac, and bupivacaine in pediatric tendon surgery: a randomized controlled trial
Tuning Exciton–Phonon Coupling by Isotope Engineering: Regulated Charge Carrier Evolution in Carbon Nitride Photocatalysts
ABSTRACT Polymeric carbon nitride (C 3 N 4 ) has emerged as a promising photocatalytic material, yet its photoconversion efficiency remains constrained by strongly bound excitons that impede free carrier generation. While structural or electronic modifications can lower the exciton binding energy, the detrimental role of exciton–phonon coupling in nonradiative decay is often overlooked. Here, we present an isotopic strategy by integrating hydrothermally synthesized deuterated carbon dots (d‐CD) into C 3 N 4 to construct d‐CD/C 3 N 4 composites, with negligible alteration to the bandgap. Temperature‐dependent photoluminescence spectroscopy reveals that deuteration not only reduces the exciton binding energy from 72.8 to 60.9 meV, but also weakens the exciton–phonon coupling strength from 977 to 794 meV. This suppression of exciton–phonon coupling mitigates nonradiative recombination, prolonging the charge carrier lifetime from 0.117 to 0.570 ms, as confirmed by transient photovoltage measurements. The stabilized long‐lived carriers further facilitate electron extraction, evidenced by enhanced electron transfer to methyl viologen as an effective electron mediator. As a result, d‐CD/C 3 N 4 exhibits a 1.7‐fold improvement in photocatalytic hydrogen evolution relative to its non‐deuterated counterpart. These findings underscore isotope engineering as an effective approach to regulate exciton–phonon interactions and charge carrier dynamics across the ps−ms timescale, offering a new dimension for tuning the optoelectronic behavior of C 3 N 4 ‐based photocatalytic systems.
Multidimensional Healthy Adult Scale: Development and validation of a measurement tool to understand how the Healthy Adult works in a Turkish population
Healthy Adult (HA), a key schema therapy construct, represents the individual’s ‘healthy’ state, characterized by balancing personal and others’ needs within a realistic perspective. We developed the Multidimensional Healthy Adult Scale to explore how various dimensions of the HA contribute to different aspects of well-being and tested its factor structure and psychometric properties. Data were collected from 472 participants (24.1% male, 75.5% female) between the ages of 18 and 60. The items of the scale were generated based on a qualitative study conducted in Türkiye. Data were analyzed using Confirmatory Factor Analysis and Structural Equation Modeling in Lavaan, which demonstrated a strong fit for the measurement and predictive models. The Bond, Balance, and Battle factors, along with the overarching HA, showed good fit. Low Balance scores were associated with higher psychopathology and negative affect, while high Battle scores were associated with greater life satisfaction and positive affect. Although Bond correlated positively with Balance and Battle, high Bond scores, when controlling for the others, were linked to increased psychopathology and negative affect. These results provide evidence for a multidimensional structure of the HA. Further validation of the scale and clarification of Bond’s role is needed for clinical insights.
Glycan-binding properties of SARS-CoV-2 spike proteins: interactions with aminoglycoside antibiotics
Harnessing Exciton Flux With a Single‐Stranded DNA‐Programmed Nanodevice
ABSTRACT Natural and artificial nanosystems rely on functional module assembly, yet overcoming thermodynamic incompatibility in atomically precise nanodevice integration remains challenging. Here, we construct a single‐stranded DNA (ssDNA)‐directed nanodevice that achieves 94.3% quenching efficiency by harnessing asymmetric π–π interactions to split exciton energy levels. DNA spatial confinement orchestrates hydrophobic, covalent, and π–π interactions, enabling precise component arrangement. This nanodevice comprises a light‐harvesting engine, a vibrational metal nanocluster actuator and a programmable ssDNA. Asymmetric π–π interactions between fluorophore and metal nanocluster in DNA spatial confinements split fluorophore energy levels, directing exciton flux. Complementary DNA strands modulate engine‐actuator coupling, enabling enthalpy‐driven switching between radiative and non‐radiative pathways. By tuning DNA length and nanocluster ligands, we achieve continuous control over energy transfer efficiency. This programmable platform, manipulating non‐radiative decay via π–π interactions, establishes vibrational control as a general paradigm for nanoscale energy transduction.