Sleep plays a crucial role in memory consolidation. In everyday educational settings, students often rapidly review studied material just before sleep, but the effect of this strategy on subsequent memory has never been investigated. Here we assessed whether briefly re-reading a studied text immediately before a retention interval spent asleep or awake affects its delayed recall. In a mixed design, 34 university students were assigned to a Sleep or a Wake group. Eight hours after studying and immediately recalling a brief text, participants either quickly re-read it (for 1 min) or not, before an approximately 8-h retention interval spent asleep or awake. Main effects of Group and Condition were found (better performance in Sleep vs. Wake and in Re-reading vs. No Re-reading conditions, respectively), as well as an interaction Group x Condition. Crucially, only participants who re-read the text before sleeping showed a net improvement in recall relative to their own initial performance, whereas performance slightly deteriorated in all other conditions. These findings extend previous work on sleep-related prose memory consolidation by showing that a brief pre-sleep review, ecologically similar to common student study habits, can boost subsequent memory beyond mere protection from forgetting. They suggest that quick reviews of studied material at bedtime may represent an effective and practical strategy for memory enhancement in real-life learning contexts.
A series of alkaline earth niobates, ANb2O6 (A = Ba, Sr, and Ca), was synthesized with the aim of probing the effect of A-site alteration in columbite structures. The prepared materials were thoroughly characterized to understand the structural, morphological, and physicochemical properties. The Rietveld refinement and Raman spectroscopy reveal that CaNb2O6 (CNO) crystallizes into a regular columbite structure; however, SrNb2O6 (SNO) and BaNb2O6 (BNO) deviate from the columbite structure owing to their bigger A-site ions. The prepared products were utilized for the photocatalytic reduction of Cr6+ and the degradation of several types of antibiotics, like ciprofloxacin, doxycycline, and metronidazole. The photocatalytic activity of the synthesized catalysts is in the order of SNO > BNO > CNO in all the pollutants. This can be attributed to the higher band gap and the lower rate of recombination in SNO. Additionally, these niobates exhibit ferroelectric behavior in the order of SNO > BNO > CNO. The projected density of states (PDOS) reveals a stronger p-d hybridization and higher empty d states, facilitating better ferroelectric behavior in SNO. This significantly lowers the charge recombination rate and boosts its photocatalytic activity. Therefore, this study correlates photocatalytic activity with the intrinsic ferroelectric nature of these niobates and opens up the scope for exploring these materials as a new class of ferroelectric photocatalysts.
The preparation effect (PE) describes enhanced attention and faster responses of dot-probes when stimuli are expected to appear. Prior work portrayed PE as a rigid, mandatory, process-all mechanism that boosts alertness for any upcoming event, largely insensitive to stimulus relevance, valence, or individual differences. The present study tested key boundary conditions of this effect across three experiments. In Experiment 1, we manipulated distractor probability and found a robust PE only under complete certainty (100% distractors), but not under a probabilistic context (50%), indicating that strong temporal expectations are required to trigger preparation. There was no difference between latencies of probe-dot detection under 25 and 75% distractor probability (Exp. 1b). Experiment 2 aimed at testing the PE across time, and distractor presence (0 or 100%) was manipulated between subjects. Dot-probe responses were consistently faster in the distractor group than in the no-distractor group, and this advantage remained stable across blocks, suggesting that the PE constitutes a durable alerting mode that, unlike other proactive effects, does not decay over time. Experiment 3 replaced the dot-probe onset detection with an offset-detection probe and found no significant RT benefit under this condition. Together, these findings demonstrate both the robustness and the limits of the PE. They also highlight the similarities and differences between the PE and other proactive control and phasic alertness effects, and call for a more nuanced explanation that considers both observers' temporal expectations and probe demands.
Nickel (Ni) contamination is an increasing environmental concern that negatively affects plant growth, physiological performance, and the biosynthesis of medicinally important secondary metabolites. The use of natural biostimulants such as chitin and nitric oxide (NO) has emerged as a promising strategy to enhance plant tolerance against heavy metal stress. Therefore, this study investigated the potential of chitin and NO to enhance the physiological and phytochemical responses of Andrographis paniculata under Ni stress. The study was designed to assess the effects of varying concentrations of chitin (0, 15, and 30 µM) and NO (0, 0.5, and 1 g/L) on several growth parameters, including photosynthetic pigments, total phenolic content, total flavonoid content, protein accumulation, key secondary metabolites (andrographolide, neoandrographolide, and 14-deoxy-11,12-didehydroandrographolide), and the expression of isoprenoid biosynthesis-related genes (HMGR, HMGS, DXR, and DXS) in A. paniculata under different levels of Ni stress (0, 1.5, and 3 mM). The results showed that Ni stress significantly reduced chlorophyll, carotenoid, phenolic, and protein contents, whereas it altered secondary metabolite profiles and gene expression patterns. Application of NO and chitin significantly improved chlorophyll a, chlorophyll b, carotenoids, total phenols, and protein content under Ni stress conditions. In addition, NO and chitin treatments enhanced the accumulation of key bioactive compounds and positively regulated the expression of genes involved in terpenoid biosynthesis pathways. Overall, the findings indicate that NO and chitin alleviate Ni-induced stress in A. paniculata primarily through improving physiological performance and enhancing the accumulation of non-enzymatic antioxidant compounds such as phenolics and flavonoids, thereby contributing to improved metabolic stability and secondary metabolite production under heavy metal stress. Andrographis paniculata emerges as a valuable medicinal-industrial species with diverse pharmaceutical applicationsChitin-nitric oxide synergy significantly boosts nickel stress tolerance and phytochemical production in A. paniculataNickel stress upregulates terpenoid biosynthesis genes (HMGS, HMGR, DXS, DXR), amplified by chitin-NO elicitation.
The rapid integration of wireless communication technologies in modern microgrid systems has significantly improved operational flexibility, decentralized control, and real-time monitoring. However, this advancement also introduces critical cybersecurity vulnerabilities, particularly Line Outage Masking (LOM) attacks that can manipulate system topology information and mislead control decisions. To address this challenge, this paper proposes a Lightweight Transformer-Based Edge AI (LTBEA) framework for accurate and low-latency LOM attack detection. The proposed model combines graph-based spatial feature extraction with transformer-based temporal attention mechanisms to effectively capture complex interdependencies among grid nodes and time-series measurements. A lightweight CNN-1D module is employed for local feature extraction, while a GRU-lite unit enhances temporal sequence modeling under dynamic load conditions. The system is deployed on edge computing devices to enable real-time analytics, reduce communication overhead, and ensure scalability in resource-constrained environments. Furthermore, adaptive filtering and normalization techniques improve robustness against noise and measurement uncertainties. The experimental results show that the proposed LTBEA framework has a detection accuracy of 97.8% with a false positive rate of only 1.9% and an average detection latency of 12ms under the noisy wireless communication environment, while having excellent robustness. It is shown that the framework is consistently stronger than traditional detection methods, regardless of the size of the microgrid and the type of attack, and that the Low Rate of False Alarm (LRFA) Line Outage Masking (LOM) attack can be reliably detected with minimal computation. The results validate the feasibility of the LTBEA as an effective and usable cybersecurity solution to boost the operational security, reliability, and resilience of next-generation wireless microgrid networks when they are deployed at the edges of the network.
Incidental gallbladder cancer (IGBC) is often diagnosed only during or after cholecystectomy, and preoperative identification remains challenging. This study preliminarily explored factors contributing to IGBC missed diagnosis and attempted to develop an ultrasound radiomics‑based identification model. A retrospective cohort of 62 IGBC and 78 non‑incidental GBC (NIGBC) patients who were consecutively enrolled between 2016/01-2025/12 was analyzed. Clinical, laboratory, imaging, pathological, and immunohistochemical features were compared. From ultrasound images, 1220 radiomics features were extracted; after stability (ICC > 0.75), redundancy removal (Spearman |ρ|> 0.90), and LASSO regression, nine features were retained. Seven machine learning algorithms were used to exploratorily build radiomics‑only models. Potential clinical predictors were identified by logistic regression, and a clinical‑only and a combined model were attempted. Performance was preliminarily evaluated using area under the curve (AUC). IGBC showed higher gallstone prevalence (91.9% vs. 53.8%, P < 0.001) and a "benign masquerade" laboratory profile (higher albumin (ALB), high-density lipoprotein cholesterol (HDL‑C); lower ratio of albumin to globulin (RAR); fewer elevated CA19‑9). Pathologically, IGBC was predominantly infiltrative (77.4% vs. 28.2%, P < 0.001), had earlier T stage compared to NIGBC (27.4% vs. 12.8%, P = 0.007), and exhibited lower Ki‑67 high expression (67.7% vs. 83.3%, P = 0.031) and weaker Topoisomerase II-alpha (Topo II) staining (P = 0.005). The extreme gradient boosting (XGB) radiomics model achieved a validation AUC of 0.865 (accuracy 0.833), suggesting potential discriminative ability. Gallstones (OR = 9.484) and growth pattern (OR = 0.230) might be independent clinical predictors. The clinical‑only model had AUC 0.830, and the combined model AUC 0.860, with no significant benefit over radiomics‑only. Preoperative missed diagnosis of IGBC may be associated with gallstone‑related inflammation, infiltrative growth, early T stage, seemingly normal laboratory findings, and low proliferation marker expression. Although conventional ultrasound hardly identifies IGBC, its tumor heterogeneity might be quantified by radiomics. The XGB model showed preliminary ability to distinguish IGBC from NIGBC and holds potential as a non‑invasive tool for preoperative risk stratification, but findings require validation in larger multicenter cohorts.
Achieving "zero hunger," the second sustainable development goal, is challenging due to climate change, weather and climate extremes, and unabated human population growth. Understanding likely changes in spatial crop suitability and yield for maize, rice, wheat, and soybean using the FAO-Ecocrop and eXtreme Gradient Boosting (XGBoost) models is imperative for sustainable agri-food systems. Our results reveal a northward shift in climate suitability as the optimal and suitable regions decrease and the unsuitable and marginal areas expand more profoundly in the far future (2061-2100) than in the near future (2021-2060) relative to the 1970-2000 baseline for all crops under all considered emission scenarios. Heterogeneous yield changes are observed in most parts of the globe, though with consistent regional outcomes. Across all emission scenarios, projections for all four staple food crops show spatially consistent declines in climate suitability and yield within major tropical producing zones, contrasted by yield gains in extratropical regions. Currently, rice can potentially be optimized in over 40% of the agricultural land, followed by wheat (30%), maize (25%), and soybeans under the integrated food systems. Future climate is anticipated to heterogeneously alter these land fractions by ~12%, ~8%, and ~13% in maize, rice, and wheat by the end of 21st century, consequently affecting the global food trade and food security. To reduce crop yield uncertainties, climate-smart agricultural interventions, improved agronomic practices, and the optimization of technological advancements are paramount in the global south. Improvement in climate models' parameterization schemes and integration of multi-crop modelling approaches is imperative in designing food policies that bolster global food security.
Immune checkpoint blockade (ICB) has emerged as a cornerstone therapy for triple-negative breast cancer (TNBC); however, its clinical efficacy remains suboptimal due to the immunosuppressive tumor microenvironment (TME). TNBC is characterized by persistent activation of cancer-associated fibroblasts (CAFs) and infiltration of immunosuppressive cells, particularly tumor-associated macrophages (TAMs). Notably, the Hedgehog (Hh) signaling pathway plays a crucial role in both CAF activation and TAM polarization. To address these limitations, we developed an erythrocyte membrane-camouflaged biomimetic co-delivery nanoplatform encapsulating Vismodegib and BMS-1 (designated as E-V/B@NM). In vitro and in vivo studies demonstrated that the fabricated E-V/B@NM significantly accumulated in tumors and controllably released drugs in response to the acidic TME. The therapeutic agents effectively inhibited Hh pathway activation, which consequently suppressed the secretion of extracellular matrix (ECM) components by CAFs, and repolarized M2-like TAMs toward immunostimulatory M1 phenotype. Synergizing with PD-1/PD-L1 inhibition, this strategy significantly enhanced dendritic cells (DCs) maturation and antigen-presenting function, boosted CD8+ T cell infiltration and immune response, and effectively suppressed primary tumor progression and metastasis in TNBC murine models. Our findings highlight that the biomimetic nanoplatform based on E-V/B@NM remodels the TME by reprogramming both CAFs and TAMs, thereby overcoming immunosuppression and improving ICB efficacy in TNBC.
DNA fix needs BRCA1 and BRCA2. These genes stop bad cell growth. They sit on links 17 and 13. Bad changes in some genes raise the risk of womb cancer. This is true for a type called serous. These faults also boost the chance for womb and breast bad growth. BRCA1 faults lead to more severe womb tumors than BRCA2 faults. Drugs like tamoxifen increase this risk more. More breast trouble adds risk, too. The risk of womb cancer in a lifetime is still small - not quite 3% for BRCA1 folk and just 1 or 2% for BRCA2 folk. Yet, BRCA1 faults might bring worse sorts of womb growth. These have bad cell traits and weak fix tools. Now, rules do not say all BRCA folk must have their wombs removed or get womb checks. They say to talk hard about risks. Think of drug use, weight, and past breast issues. New tools look at many cell parts at once. This helps grasp how BRCA links to bad growth. It lets us pick just the right cures, like PARP drugs. But past studies differ. We need wide, long studies with many groups of folks. This will show the real womb cancer risk. So, BRCA faults do play a small but key part in a bad womb growth type. But they mainly boost breast and egg trouble.
Hyperbranched polymers are well-established for boosting the toughness of epoxy-based systems. In this work, a novel amino-terminated hyperbranched polysiloxane (HBPSi-NH2) was synthesized and then incorporated into carbon fiber/epoxy (CF/EP) composites to improve their mechanical performance. A two-step synthetic route was involved: linear epoxy-terminated polysiloxane (LDP) was initially prepared as the intermediate, and the target HBPSi-NH2 was then obtained via nucleophilic addition between LDP and diethylenetriamine. Structural characterization via FTIR and 1H NMR demonstrated that target HBPSi-NH2 was obtained, while quantitative analysis based on 13C NMR data showed that the degree of branching of the product was 0.78. The influence of adding HBPSi-NH2 as a toughener to bisphenol A epoxy resin (E51) blends was systematically investigated. The incorporation of 3 wt% HBPSi-NH2 marginally reduced tensile and flexural strengths of the toughened epoxy. However, it significantly enhanced the impact strength from 26.7 kJ m-2 to 56.2 kJ m-2. Incorporating the toughener into CF/EP composite fabrication effectively improves tensile strength, flexural strength and short-beam shear strength, especially with low HBPSi-NH2 amounts (3 and 5 wt%). This study offers a facile strategy to simultaneously improve the mechanical strength and interfacial properties of CF/EP composites.
Machine learning models that predict hospital admission at triage may support patient flow forecasting, yet the effects of covariate drift, concept drift, and retraining on long-term performance are poorly understood. We developed an Extreme Gradient Boosting (XGBoost) model using deidentified data from all presentations to a metropolitan hospital in Western Australia. Training and validation included 2016 and 2017 presentations (n = 133,814), with rolling quarterly testing from 2018 to 2023 (n = 455,496). Two adaptive strategies were evaluated: quarterly and half-yearly retraining. Covariate drift was assessed using univariate and multivariable analyses, and reporting adhered to TRIPOD + AI and MINIMAR standards. Substantial drift was observed both between training and testing datasets and across the six-year testing period. The base model achieved a mean AUROC of 0.875 (range 0.844-0.887) and mean daily bed error of 7.42 beds (range 0.47-13.1). Retrained models demonstrated improved discrimination (mean AUROC 0.892 and 0.893) and reduced bed error (4.02 and 4.48 beds per day) for quarterly and half-yearly retraining, respectively, with similar calibration and classification performance. Covariate drift meaningfully degraded calibration but not discrimination metrics over time. Simple retraining improved discrimination and reduced calibration concept drift, underscoring the importance of retraining to address drift for temporal model deployment.
Insulin resistance (IR) represents a critical metabolic complication in iron-deficient children, yet existing predictive models target general or overweight pediatric populations and do not account for iron-deficiency as a distinct risk modifier. This study aimed to develop and externally validate machine learning (ML) models using routinely available clinical parameters to address this diagnostic gap. We utilized data from 222 iron-deficient children and adolescents aged 6 to 17 years from the China Health and Nutrition Survey (CHNS) for model training, and 125 cases from two hospitals for external validation. Iron-deficiency was defined using age- and sex-specific soluble transferrin receptor (sTfR) thresholds, with exclusion of elevated high-sensitivity C-reactive protein (hs-CRP) (≥5 mg/L) or missing metabolic variables. IR was defined as Homeostatic Model Assessment for Insulin Resistance (HOMA IR) exceeding 3.0. Least Absolute Shrinkage and Selection Operator (LASSO) regression selected nine predictors from 27 candidate variables (demographics, anthropometrics, blood pressure, hematology, glucose metabolism, lipids, hepatic and renal function). Four ML algorithms [logistic regression (LR), random forest (RF), k-nearest neighbor (KNN), and extreme gradient boosting (XGBoost)] were developed and evaluated by area under the curve, sensitivity, specificity, and calibration, with five-fold repeated cross-validation for internal validation. SHapley Additive exPlanations (SHAP) analysis quantified predictor contributions. XGBoost achieved optimal discriminative performance with an external validation area under the receiver operating characteristic curve (AUC) of 0.940 [95% confidence interval (CI): 0.889-0.991], outperforming other algorithms. RF demonstrated the highest training AUC (0.993, 95% CI: 0.987-1.000) with near-perfect sensitivity (0.985, 95% CI: 0.920-1.000) but showed limited generalization capacity given minimal training-validation divergence. LR and KNN achieved lower validation AUC values of 0.832 (95% CI: 0.743-0.922) and 0.823 (95% CI: 0.740-0.905), respectively. XGBoost was selected as the final model based on superior specificity (0.967, 95% CI: 0.906-0.993) and tighter CIs, indicating more stable performance estimation. Fasting glucose (mean |SHAP| =0.707) and triglycerides (0.383) emerged as dominant predictors, while albumin demonstrated a protective association [odds ratio (OR) 0.86, 95% CI: 0.78-0.95]. This study establishes an externally validated, interpretable ML framework for predicting IR among iron-deficient youth using routine clinical data. While the XGBoost model demonstrates promising discriminative performance and geographic generalizability, the modest sample size and single-province validation limit immediate deployment readiness. Prospective multi-site validation is required before any consideration of clinical implementation as a developmental screening framework.
The adsorption-coupled degradation of dyes offers an attractive strategy for the remediation of industrial dye wastewater. Herein, a metal-free, porous ternary-composite aerogel was rationally designed and constructed using graphitic carbon nitride (g-C3N4), graphene oxide (GO), and β-cyclodextrin (β-CD). The g-C3N4/GO/β-CD aerogel (denoted as CNGOCD) exhibited excellent cooperative adsorption enrichment and photo-driven degradation capabilities for several organic dyes, including rhodamine B, methylene blue, and neutral red, achieving an adsorption capacity of 653.2 mg/g and a removal rate of 97.1%. These results outperformed traditional reduced graphene oxide (rGO) and rGO/β-CD aerogels. After five cycles, the CNGOCD aerogel maintained stable performance without a significant decrease in either adsorption or photocatalytic activity. Spectral analysis, electrochemical measurements, and density functional theory (DFT) calculations were employed to elucidate the adsorption mechanisms and the enhanced photocatalytic performance of the aerogel. Our findings suggest that GO enhances charge separation in g-C3N4 to boost photocatalytic activity, while β-CD prevents GO stacking, creating a porous architecture rich in active sites for rapid adsorption. Furthermore, this metal-free aerogel is eco-friendly due to no risk of heavy metal leaching, causing secondary pollution. The insights gained from this work provide a solid foundation for designing advanced, multi-purpose materials aimed at effective pollutant decontamination.
The rapid spread of the competing weed L. arvensis poses a major threat to wheat production; therefore, modern risk assessment methods are necessary for its management. This study developed and compared machine learning models (Random Forest [RF], Boosted Regression Trees [BRT], and Maximum Entropy [MaxEnt]) to evaluate habitat suitability for L. arvensis, a dominant weed in the wheat cropping systems of Pakistan's semi-arid regions. For this purpose, weed data from 402 wheat fields, along with 20 environmental factors, including topography, climate, soil characteristics, anthropogenic factors, and proximity metrics, were analysed. Soil texture (silt and clay), soil chemistry (EC, OM, TDS), and rainfall patterns were identified through a partial least squares (PLS) algorithm as major factors affecting the species distribution. The ROC-AUC results showed that MaxEnt (AUC = 0.93) and RF (AUC = 0.92) performed slightly better than BRT (AUC = 0.86). All models identified the eastern and southeastern regions as the main areas of highly suitable habitat. Although these models are reliable, their predictions may be affected by changes in environmental factors in cropland. These results demonstrate that machine learning methods are effective for mapping weed distribution and provide a scientific foundation for sustainable weed management in these regions.
Accurate species distribution modelling is essential for conservation planning, particularly for river-dependent species whose habitats are poorly captured by conventional distance-based predictors. The endangered Scaly-sided Merganser (Mergus squamatus) relies on linear freshwater habitats during winter, yet riverine habitat structure is often oversimplified in broad-scale species distribution models. Here, we developed a multi-algorithm modelling framework to map wintering habitat suitability for the species in the Dongting Lake Basin, China. Occurrence records were compiled primarily from systematic surveys and spatially thinned to reduce sampling bias. We compared MaxEnt, random forest, generalized additive models, and boosted regression trees under five-fold spatial block cross-validation, and generated an ensemble prediction across algorithms. The final predictor set emphasized hydrological and topographic attributes, including maximum water width, reservoir capacity, density of the river network, valley depth, landform type, and channel-network base level. Predictor importance and response curves indicated that wintering habitat suitability was shaped mainly by open-water scale, terrain position, valley structure, and regulated water storage. The ensemble prediction identified priority high-suitability areas mainly along the middle and lower Yuan River and the upper Xiang River, with smaller local patches along the Li and Zi Rivers and selected reservoir systems. These findings suggest that hydrological and topographic predictors can enhance the ecological representation of river-dependent waterbird habitats in SDMs and provide spatial evidence for targeted river-reach conservation.
Efficient separation of a propylene (C3H6) and ethylene (C2H4) mixture is very essential in the petrochemical industry but remains a major challenge due to their similar physicochemical properties. Herein, we report a diffusion-trap mechanism in a metal-organic framework (Cu-TBDA) with one-dimensional channels and functionalized side nanocages for excellent C3H6/C2H4 separation. The suitable channels and confined cavities in Cu-TBDA simultaneously boost the mass transfer efficiency and provide strong binding sites for C3H6 molecule. Thus, at 298 K, Cu-TBDA achieves ultrahigh C3H6 uptake of 2.06 mmol g-1 at 0.01 bar, and record Henry selectivity of 44.0 and ideal adsorption solution theory (IAST) selectivity of 39.8 for C3H6/C2H4 mixtures. The kinetic experiments proved Cu-TBDA possesses fast C3H6 adsorption kinetics (0.66 min-1) and high kinetic C3H6/C2H4 selectivity (4.57). Dynamic breakthrough tests confirm Cu-TBDA can gather high-pure C2H4 (> 99.95%) from C3H6/C2H4 mixtures with benchmark C2H4 productivities and C3H6/C2H4 separation factors, along with efficient C3H6 (> 99.90%) recovery during desorption. Additionally, Cu-TBDA has good structure stabilities and can be synthesized through the simple reflux technique, which renders it a promising adsorbent for practical C3H6/C2H4 separation. This work sets a new benchmark for achieving precise and efficient gas separation in industrial applications.
In this study, we used the E. coli-expressed recombinant influenza H1N1 receptor-binding domain (H1N1-RBD; MW = 27.4 kDa) as a model to explore how stable oligomers or aggregates can be produced by modulating sample conditions (temperature and pH), and to examine whether such colloidal/conformational modulations enhance immunogenicity toward vaccine antigen design. We thus analyzed the biophysical properties of the protein under three different pHs at 25 °C and 37 °C using DLS, SLS, CD, and tryptophan fluorescence spectroscopy, and analyzed the immune response in mice. We found that the E. coli-expressed H1N1-RBD was predominantly monomeric at pH 4.7, oligomerized at pH 6.0 (Rh ∼ 20 nm), and formed large aggregates (Rh < 1000 nm) at pH 7.4. Immunization studies in Jcl:ICR and BALB/c mice revealed that the oligomeric form (pH 6.0) elicited the highest IgG titers, whereas the monomeric (pH 4.7) and the aggregated (pH 7.4) forms induced weaker antibody responses. Despite this, all three preparations generated neutralizing antisera, with neutralization potency increasing in the order oligomers < monomers < aggregates, in the absence of adjuvants. Notably, the aggregated H1N1-RBD conferred protection against live viral challenge comparable to that of an inactivated influenza vaccine, as demonstrated by plaque reduction assays in MDCK cells and in vivo challenge experiments in BALB/c mice. Flow cytometric analysis further revealed robust long-term immune memory, with central memory CD4+ T cells accounting for 52.53-65.04% and CD8+ T cells for 23.81-37.85% of the population. Overall, our findings highlight the potential of E. coli-produced proteins as vaccine antigens and suggest that pH-controlled protein oligomerization/aggregation can significantly boost immunogenicity and the neutralizing efficiency of antisera.
Efficient generation of reactive oxygen species (ROS) through synergistic regulation of charge transfer (CT) and energy transfer (ET) pathways is key to enhancing photocatalytic reaction rates. Nevertheless, simultaneously optimizing both singlet (S1) and triplet (T1) exciton utilization to maximize ROS generation and its subsequent effective exploitation remains challenging. Herein, a series of amphiphilic perylene-based nanomicelles is designed via donor engineering, in which spatial coupling between dual-path ROS generation and pollutant oxidation is achieved. Experiments and theoretical calculations certify that the micelle with triphenylamine as the donor can boost the CT efficiency of S1 to engender superoxide radical (•O2 -) production, while concurrently improving the intersystem crossing (ISC) efficacy from S1 to T1 to prolong the T1 lifetime and activate the ET process for singlet oxygen (1O2) yielding. Additionally, the pre-enrichment of pollutants by micelles shortens the migration distance of ROS and extends their lifetime, thereby maximizing the directed utilization efficiency of ROS. The strategy that combines photophysical properties with structural optimization, successfully surmounts the confinements of organic photocatalytic materials in solar-driven chemical reactions.
The isotopic composition of atmospheric carbon dioxide (δ13Catm) provides insights into the terrestrial carbon cycle. However, long-term global δ13Catm maps with both high spatial resolution and continuous temporal coverage remain scarce. Here, we present a new global terrestrial dataset of monthly δ13Catm isoscapes from 2001 to 2020 at 0.05° spatial resolution, developed by integrating in situ observations with optimized 4D CO2 concentration fields from inversion outputs, reanalysis data, and geographic information using machine learning. Among four tested models, the Gradient Boosting Machine demonstrated the highest predictive performance under random validation (R2 = 0.80, RMSE = 0.12‰) and maintained robust performance across three spatially independent validation frameworks (R2 = 0.56-0.67, RMSE = 0.16‰-0.19‰). Key predictors were air temperature and atmospheric CO2 concentration. The resulting global terrestrial isoscapes reveal strong spatial and temporal heterogeneity. Model predictions closely align with National Oceanic and Atmospheric Administration (NOAA) marine boundary layer (MBL) observations in terms of trend magnitude, seasonal amplitude (< 0.13‰ deviation), and latitude gradient (< 0.2‰). In our study, δ13Catm seasonal amplitude varies from 0.06‰ in Southern Hemisphere mid-latitudes to 0.6‰ in Northern Hemisphere high latitudes, indicating strong hemispheric asymmetry. Moreover, over 99.8% of global terrestrial grid cells show negative trends in all seasons, with a global terrestrial average annual depletion rate of -0.030‰ ± 0.0006‰ year-1. The trend shows stronger depletion during summer and autumn, reaching its peak in August (-0.035‰ ± 0.0012‰ year-1), while spring and winter seasons remain comparatively stable. This study delivers a long-term, high-resolution global terrestrial δ13Catm isoscape dataset, offering a valuable tracer for carbon cycle research and, importantly, robust data support for large-scale investigations of carbon-water coupling in terrestrial ecosystems.
Nutrient imbalances, soil salinity, and shrinking arable land threaten global food security, driving demand for sustainable biofertilizer alternatives to chemical inputs. Aquatic ecosystem-derived biofertilizers such as Magnetospirillum gryphiswaldense (MSR-1) are promising sustainable substitutes and show strong agricultural potential due to their stress tolerance, adaptability, and plant growth-promoting traits. This study investigated the ability of MSR-1 to enhance the growth and productivity of tomato and paddy under normal, iron-deficient, and saline conditions. MSR-1 was cultured in modified Magnetospirillum Growth Medium (MSGM) under microaerophilic conditions, with SEM confirming its spiral gram-negative morphology and successful, non-destructive colonization on tomato and paddy roots and leaves. Moreover, HR-LCMS profiling of root exudates identified chemoattractant compounds such as quinic acid, tryptophan, quercetin, glucosinolates, and strigolactones, promoting bacterial attachment. Further, Magnetospirillum liquid biofertilizer (MLB) was formulated from MSR-1 cultures (1.5 × 108 cells/mL) and applied at 20-100% concentrations (25 mL/pot). Among the treatments, 20% MLB gave the best results under normal conditions, whereas 60% MLB was more effective under iron-deficient and saline stress conditions. In tomato, 20% MLB increased shoot length (73.5 cm), chlorophyll content (4.5 mg/g), and fruit yield (1066.95 g/plant). Under stress, 60% MLB improved fruit yield (760-800 g/plant) and boosted antioxidant enzymes (SOD 75 U/mg; CAT 15.5 U/mg). In paddy, 20% MLB enhanced shoot and root length (66.0 and 15.13 cm), while 60% MLB under stress increased growth, carbohydrates, proteins, amino acids, phenols, and antioxidant enzymes (SOD/CAT 49.63/19.83 U/mg). Overall, MSR-1 offers a sustainable, effective biofertilizer option for managing soil salinity and iron deficiency.