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Health-related quality of life and its associated factors among patients with hypertension based on the Shapley value method
Interactive effects of selenium and fertigation regimes on strawberry performance and biofortification under salinity stress
Abstract Salinity is a significant constraint for strawberry production worldwide. This study investigated the effects of selenium (Se) supplementation and different fertigation regimes on strawberry plant performance under saline conditions. The experiment was conducted as a three‑factor factorial arrangement in a completely randomized design with four replicates. Rooted daughter plants of the cv. ‘Paros’ were grown in 3-L pots with a perlite–coir pith mixture and exposed to two salinity levels (control or without NaCl and 40 mM NaCl). Se was supplied as sodium selenate (0 or 1 mg L − 1 ). The third factor was fertigation regime at four levels: morning Hoagland (M-H, 8:00 AM, daily), afternoon Hoagland (A-H, 8:00 PM, daily), both morning and afternoon Hoagland (MA-H, 8:00 AM and 8:00 PM, every other day) and mixed morning/afternoon Hoagland at a 1:2 ratio (MA-1/2H, 8:00 AM and 8:00 PM, daily). Salinity and Se were applied via the nutrient solution. Under salinity, the M-H treatment with Se was associated with reduced yield loss (24%, P < 0.05) and higher root and shoot dry weights compared to other fertigation regimes. This treatment also showed associations with enhanced antioxidant responses, including free radical inhibition (80%), higher anthocyanin content, and increased superoxide dismutase activity, along with improved K and Ca accumulation in shoots. Se supplementation increased fruit Se concentrations to 2.2–3.1 mg kg − 1 dry weight. These results indicate that combining Se application with M-H delivery is a promising strategy to mitigate salinity-induced stress in controlled environment systems. Future research is required to evaluate these regime-dependent responses in other cultivars and to optimize Se doses for biofortification while maintaining peak plant performance.
Synthesis, performance, and DFT insights of sustainable waste-derived S/N-doped alumina as an antiaging coating for paper sheets
Abstract Using waste aluminum cans is a sustainable method created to attained alumina doped with sulfur and nitrogen as an antiaging coating for paper sheets. Acidic digestion, precipitation, calcination, and thiourea-assisted microwave doping were used to create the doped alumina, which produced nanoscale S/N-alumina particles. These particles were applied to paper sheets at different concentrations and immersion times after being mixed with a hydroxyethyl cellulose-based treatment. Mechanical testing, FTIR, SEM, flammability testing, and density functional theory calculations were used to assess the coated sheets both before and after accelerated thermal aging. Tensile strength, elongation, flame retardance, and resistance to thermal aging under accelerated conditions were all much improved by the coating, which performed best at 2% concentration and 15 min of immersion.
Microfluidic pulse oscillator enabled by a shapeshifting liquid metal capacitor
Cancer immunotherapy targeting murine myeloid cells requires endosomal pattern recognition
Abstract Immunotherapy is now an established and efficient treatment option for many cancer patients. However, a proportion of patients still experience poor outcomes due to treatment resistance. Thus, a clear understanding of key mechanisms of resistance is needed for the development of new treatments. Here, we employ mouse models to explore an immunotherapeutic approach based on anti-MARCO (αMARCO) and anti-PD-L1 (αPD-L1) antibody-mediated targeting of tumor-associated macrophages (TAM). We demonstrate that effective immunotherapy relies on a functional endosomal pattern recognition machinery. We determine that endosomal Toll-like receptors (TLR), specifically TLR9, precondition macrophages to respond to αMARCO treatment by regulating the transcription of inflammasome components. Absence of TLRs renders TAMs unresponsive to treatment while retaining an immunosuppressive phenotype. Thus, we uncover the intracellular TLR signalling as a feature of immunotherapy efficacy, required to sensitise TAMs to treatment, and indicate that TLR targeting could be exploited to improve immunotherapeutic outcomes.
Tritium separation from gaseous 1,2,3H isotopologue mixtures by selective adsorption on Ag-exchanged zeolite type Y
Abstract Efficient separation of hydrogen isotopologues is crucial for applications such as the recycling of exhaust streams in nuclear fusion reactors. We report on separation of a ternary 1,2,3 H isotope mixture using thermal desorption spectroscopy (TDS), achieving an enrichment of 1:41:175 (H 2 :D 2 :T 2 ) from an initially equimolar (1:1:1) gas mixture, based on selective adsorption using an Ag(I)-exchanged zeolite type Y. Further experiments on binary hydrogen isotope mixtures validated numerical predictions of the separation efficiency for T 2 . Specifically, the high selectivity for tritium over protium of 244 makes the Ag(I)-exchanged zeolite an excellent candidate for energy-efficient isotope separation at liquid-nitrogen temperature.
Food-related energy consumption can help reveal poverty in rural Chinese households
Determinants of electron transport at asymmetric metal/molecule/metal contacts
Hyperbolic adaptive spatial-aware multivariate time series anomaly detection
Abstract Existing multivariate anomaly detection methods suffer from practical industrial limitations stemming from distribution assumptions, volatile data and scarce reliable anomaly labels. This study proposes a hyperbolic adaptive spatial-aware multivariate anomaly detection method aimed at enhancing accuracy, robustness and interpretability in real-world applications. It first constructs an adaptive hyperbolic pre-training framework to embed coupled spatio-temporal hierarchical time-series features and mine latent cross-variable correlations. Next, an adaptive graph structure discovery and dynamic sparsification module removes rigid topology restraints, autonomously learning inherent data structures and pinpointing the spatio-temporal locations of anomalies for causal interpretation. A multi-level attention module and small-sample dynamic threshold algorithm further improve model stability when handling complex signals. Built upon self-adaptive graph topology, the unified end-to-end framework integrates anomaly recognition, diagnosis and interpretation with dedicated discrimination and correction mechanisms. Experiments demonstrate 10%–30% performance improvements across diverse detection benchmarks, verifying the method’s effectiveness and advancing interpretable AI research for anomaly detection.
Autonomous bioisosteric replacement for multi-property optimization in drug design
Abstract Optimizing molecular properties while preserving biological activity is a central challenge in drug design. Bioisosteric replacement, which substitutes a molecular fragment with a chemically or biologically analogous moiety, offers a powerful strategy for fine-tuning properties without disrupting target binding. However, existing in silico approaches often rely on expert-defined modification sites or struggle to modulate multiple molecular properties simultaneously. Here, we present DeepBioisostere, a deep generative model that performs end-to-end bioisosteric replacement by autonomously selecting and substituting molecular fragments to satisfy multiple target properties. The model captures complex relationships across the molecular graph, enabling the optimization of sophisticated properties such as drug-likeness and synthetic accessibility. By learning from experimental bioassay data, DeepBioisostere proposes replacements that maintain biological activities, even generating potential bioisosteres beyond the training data. We demonstrate the effectiveness of the model in computational hit-to-lead optimization scenarios, highlighting its potential to accelerate rational molecular design without relying on expert heuristics or pre-established substitution rules.
Phase-independent wireless data aggregation via optical in-sensor computing
Observing strong optical nonreciprocity from magnetically doped Dirac semimetals
Temperature-dependent replication and sensitivity to innate immunity of human coronavirus HKU1
The sodium channel SCN2A regulates cortical excitatory and inhibitory neurogenesis
Identification of scavenger receptor BI as a scavenger of free heme that is essential for protection against hemolysis
Abstract Severe hemolysis is a life-threatening condition with limited therapeutic options. Although haptoglobin and hemopexin sequester hemoglobin and heme, these protective systems are rapidly saturated during acute hemolysis, leading to the accumulation of cytotoxic free heme. In this study, we identify scavenger receptor BI (SR-BI) as a critical mediator of free heme clearance. SR-BI binds heme and facilitates its hepatic uptake under pathological conditions. Mice lacking hepatic SR-BI exhibit impaired heme clearance and increased susceptibility to heme- and hemolysis-induced lethality. Pharmacological upregulation of hepatic SR-BI via imatinib or adenoviral delivery confers protection against heme toxicity. Using a humanized model of sickle cell disease (SCD), we further demonstrate that sickle hepatopathy significantly reduces hepatic SR-BI expression compared to non-SCD HbAA littermates, potentially increasing vulnerability to heme-induced injury. Notably, adenoviral-mediated SR-BI upregulation rescues SCD mice from heme toxicity. These findings reveal a previously unrecognized mechanism of heme detoxification via hepatic SR-BI and identify a promising therapeutic target for hemolytic disorders.