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.
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 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.
The human brain has the inherent ability to extract temporal regularities from sensory input and to synchronize with them. Language production and comprehension rely heavily on this fundamental ability. Rhythm-based training programs designed to boost language functions, especially across childhood, aim to facilitate the brain's capacity to capture regular patterns in verbal streams. This systematic review critically evaluates the evidence on the efficacy of rhythmic training in improving linguistic skills in typically developing children as well as in children with language or reading difficulties. Following PRISMA guidelines, 21 studies published between 2009 and 2024 were selected, which assessed performance in linguistic tasks after training, in comparison with a control group or condition. Twenty studies reported improved linguistic performance, mainly in phonological awareness and reading fluency (speed and accuracy). In particular, music-based rhythmic programs, which included activities such as producing movements synchronized to instruments, yielded effects comparable to validated phonological or reading training, with some studies also reporting gains in language-related cognitive and perceptual domains. Similarly, language-based rhythmic protocols, which provided verbal materials synchronized with instruments and primarily focused on children with dyslexia, reported improved reading skills. Furthermore, rhythmic priming studies demonstrated that even brief (17-second) exposures to regular rhythms enhanced subsequent syntactic processing compared to irregular rhythms. Interestingly, the effects emerged even in the absence of melodic or motor components. Overall, the findings of the reviewed studies highlight the potential of rhythmic training as a versatile tool for supporting language and reading development in children, including those with developmental language disorder or dyslexia. However, several methodological issues limit their interpretability. Before well-founded conclusions can be drawn, there is a substantial need to improve methodological rigor. Future studies should refine control conditions, standardize the range and types of outcome measures, and examine long-term effects.
Childbirth is a crucial experience that impacts women's lives, and the choice between vaginal delivery and cesarean section (C-section) is a critical decision in obstetrics. Maternal satisfaction is influenced by multiple factors since the childbirth experience is a composite of physical, emotional, and social components. Understanding patient evaluation is crucial for providing patient-centered care and improving maternity and neonatal care services. This systematic review aimed to compare patient satisfaction between vaginal and cesarean delivery patients and identify influencing factors. We performed a thorough search of databases for studies published between 2000 and 2024 on patient-reported satisfaction with vaginal delivery vs. C-section interventions. Eligible studies were assessed for methodological quality and relevance. The findings indicated that most women were satisfied with their delivery experience, with vaginal delivery leading to higher satisfaction than C-sections. Factors influencing satisfaction include maternal education, domicile, planned delivery care, healthcare professional gender, complications, partners' education, pain control measures, Apgar scores, and injuries. However, satisfaction levels were not significantly different across other maternal demographic factors or pregnancy-related characteristics. Certain features, such as planned pregnancy and excellent prenatal care, improved satisfaction with both vaginal and cesarean deliveries. The presence of a supporting companion during birth significantly boosted satisfaction levels, especially in primary care settings. Inadequate communication is associated with decreased maternal satisfaction. Therefore, healthcare practitioners should prioritize patient-centered care, good communication, and support. Targeted interventions are recommended, considering factors that influence the delivery of maternal and child care services.
Agricultural lignocellulosic waste, especially cereal straw, presents significant environmental and management challenges worldwide. Although aerobic composting provides a sustainable way to recover value, its effectiveness is limited by the resistant nature of lignocellulose. This study explains how the addition of lytic polysaccharide monooxygenase (LPMO) enhances the synergistic humification process during straw composting. Compared with the control, the inclusion of LPMO accelerates the breakdown of cellulose, hemicellulose, and lignin, increasing their breakdown by 1.69 %, 3.95 %, and 1.31 %, respectively. It also alters the microbial community, boosting key groups such as Streptomyces, Devosia, Colletotrichum, and Coniochaeta. At the functional gene level, LPMO increases pathways for carbon synthesis and decreases carbon release. Meanwhile, genes involved in ammonification, nitrification, and assimilatory nitrate reduction (ANRA) become more abundant, while denitrification genes shift, with nirK increasing and nosZ decreasing. This LPMO-driven change in functional genes ultimately helps retain carbon and nitrogen. As a result, LPMO yields higher levels of humic acids (HA) and better humification metrics, as indicated by excitation-emission matrix (EEM) spectroscopy, suggesting more mature humic substances. These findings show that LPMO promotes humification by improving substrate accessibility, shaping microbial communities, and enhancing carbon and nitrogen processes, highlighting its potential to efficiently turn waste into high-quality compost.
Dual-atom catalysts (DACs) with synergistic bimetallic sites are emerging as promising alternatives to the platinum group metal catalysts for the cathodic oxygen reduction reaction (ORR) in advanced energy conversion technologies. However, the rational design of DACs is challenged by the site heterogeneity and structural damage from prevalent pyrolysis methods, while the synthesis process of binuclear complexes with well-defined structures is cumbersome, making them rarely applied in practical devices. Bioinspired by the binuclear heme/Cu center in cytochrome c oxidase, herein we developed a facile stepwise assembly strategy to prepare Fe-Cu binuclear molecular catalysts via coordinating bipyridine (bpy)-derivative-functionalized copper complexes with nitrogen atoms of N-doped carbon substrates, followed by iron phthalocyanine (FePc) incorporation. The microenvironment of the Fe-Cu binuclear molecular catalysts was readily tuned by varying substitutes on bpy (2,2'-bpy-4,4'-R, R = H, Cl, CH3, COOH), among which the carboxylic acid-functionalized FePc@Cu(bpyCOOH)-NC demonstrated impressive high ORR activity and stability with a half-wave potential (E 1/2) of 0.92 V vs. RHE in 0.1 M KOH and a maximum power density (P max) of 147 mW cm-2 in loaded zinc-air batteries, surpassing those of commercial Pt/C (E 1/2 = 0.87 V, P max = 120 mW cm-2) under similar experimental conditions. Theoretical calculations suggested that functionalizing the Cu sites with carboxylic acid groups could significantly reduce the overpotential, thus of boosting the intrinsic ORR activity, which could be attributed to the electronic structure modulation of Fe-N4 sites and the hydrogen-bonding network formation. This work highlights the advantage of bioinspired catalyst design and assembly engineering in advancing the precise construction of DACs for energy conversion applications.
Efficient trauma assessment is essential for optimal patient care, with imaging playing a critical role in the detection of injuries. Rapid and accurate classification of traumatic spleen injuries is critical for clinical decision-making; however, manual assessment of CT images can be subjective and time-consuming, highlighting the need for objective and automated diagnostic tools. This study aims to evaluate the impact of machine learning models and radiomics features in diagnosing splenic trauma lesions on computed tomography images. A dataset of 600 computed tomography images, including individuals with mild and severe traumatic spleen injuries as well as healthy controls-was collected from the Kaggle database. An experienced radiologist segmented the axial images, and radiomics features were extracted from each designated region of interest for further analysis. Initially, 25 machine learning models were evaluated; ultimately, three-Light Gradient Boosting Machine, Ridge Classifier, and Adaptive Boosting-were selected for detailed assessment. Model performance was measured using accuracy, precision, sensitivity, specificity, area under the receiver operating characteristic curve, F1 score, and misclassification rate. The Light Gradient Boosting Machine model exhibited superior effectiveness in diagnosing mild spleen injuries, achieving an accuracy of 98%, precision, and specificity of 100%. Meanwhile, the Adaptive Boosting model demonstrated acceptable performance in diagnosing severe injuries, achieving an accuracy of 90%, precision of 92.15%, and specificity of 91%. These machine learning models exhibited remarkable capability in automatically detecting traumatic spleen injuries on abdominal computed tomography scans. By integrating radiologist expertise into the analytical framework, our method enables rapid pre-screening of a large number of cases for spleen lesions.
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.
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.
Insulin resistance plays a key role in the pathogenesis of diabetic retinopathy (DR). Although established insulin resistance markers have been shown to predict a variety of complications, the association between the estimated glucose disposal rate (eGDR) and prevalence of DR remains incompletely characterized. This study aims to examine the relationship between eGDR and DR prevalence. This cross-sectional study analyzed complete participant data (N = 1, 536) from the 2007-2018 National Health and Nutrition Examination Survey (NHANES) for all relevant information. The relationship between the insulin resistance index and self-reported DR prevalence was evaluated by using multivariate logistic regression and a restricted cubic spline (RCS) model. Subgroup analysis was conducted to assess heterogeneity across groups, and two sensitivity analyses were performed to assess the robustness of the results. In machine learning, the Boruta algorithm is applied for feature selection. The selected features are subsequently utilized by XGBoost and random forest models for DR prevalence estimation. Use the Shapley additive explanations (SHAP) value to explain the independent contribution of eGDR. In the clinical cohort, we recruited patients who visited the Second Affiliated Hospital of Anhui Medical University from September 1, 2025, to December 30, 2025. A total of 297 participants who met the inclusion criteria were finally enrolled. Multivariable logistic regression and RCS curves were used to validate the findings from the NHANES analysis. In the fully adjusted model, eGDR and self-reported DR prevalence show a significant negative linear correlation (OR = 0.79, 95% CI: 0.67-0.93, P = 0.0049). Subgroup and sensitivity analyses confirm the stability of this negative association. The Boruta algorithm identifies eGDR as a robust and important feature. Both the XGBoost (AUC = 0.773) and random forest (AUC = 0.764) models show moderate predictive performance, and eGDR has high variable importance. SHAP analysis indicates that eGDR, together with body mass index and income poverty, is a key determinant of self-reported DR prevalence. The results of the clinical cohort are like NHANES. This cross-sectional study suggested that lower eGDR is associated with a higher prevalence of self-reported DR. Accordingly, eGDR may serve as a potential marker for risk stratification rather than a causal or preventive factor. Prospective longitudinal research is necessary to confirm these findings and to explore whether a causal relationship exists.
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.
Electroporation of messenger RNA (mRNA) is an ex vivo non-integrating gene transfer technique used in immune-cell-based trials for cancer to transiently supply immune cells with multiple proteins. This technique has been used to engineer dendritic cells and B cells with tumor-associated antigens to boost the immune system of cancer patients and to redirect the anti-tumor activity of T cells and natural killer cells with immune receptors. Although gene delivery via mRNA electroporation results only in transient expression of the protein of interest, many investigators and clinicians consider it as a feasible, flexible, and safe technique, compared with stable expression methods using viral vectors. In this review, we discuss the efficacy of mRNA electroporation for gene transfer and assess the strengths and limitations of this technique for redirecting and boosting immune responses against various tumor antigens in cancer immunotherapy.
Azo compounds offer wide-range applications due to their unique structures and properties. The N═N double bond in azo compounds can form a nonrotatable plane, frequently causing spins to experience different magnetic environments, and thus leading to chemical exchange. This may result in line broadening even invisibility in the nuclear magnetic resonance spectra, thus greatly hindering detection and kinetic analysis of azo compounds. Here, a new pulse sequence, termed as FasteR Exchange with chemical-Shift Scaling for Signal enHancement (FRES3H) is developed, which scales down the chemical shift by a factor of λ via manipulating indirect dimension evolution time and converts the spin system to fast-exchange regime, thus boosting chemical exchange signal. The method can recover invisible signals in intermediate-exchange regime, facilitating the analysis of azo compounds. It can detect trace amounts of azo initiators as low as 1 mM in 2D C-H coherence spectra, thus facilitating the quality control of polymer production. And it can confirm the fact of chemical exchange differences of tert-butyl groups in azobenzene cis- and trans-isomers, thus revealing the effect of exchange kinetics on electron transport properties and providing valuable guidance for the synthesis of functional photovoltaic materials.
Photodynamic therapy (PDT) utilizes photosensitizers to generate reactive oxygen species (ROS) to kill tumors. However, the tumor's hypoxia and the limitations of a single ROS mechanism severely restrict its efficacy. This study aims to develop a synergistic nano-catalytic system (HMO) based on hollow manganese dioxide (HMnO2) loaded with a novel nitric oxide (NO) donor (Methylene Blue - NO, MB-NO), which overcomes these obstacles through multiple synergistic effects. Preliminary experiments confirmed the synthesis of HMO. Systematic studies were conducted on the chemodynamic therapy (CDT)/PDT/NO properties of HMO as well as its anti-tumor activity in vivo and in vitro. Finally, the in vivo safety of HMO was evaluated. Preparation by the HMO is 189 nm nano particle size, Zeta potential for -37 ± 1.4 mV, exhibit excellent stability. In vitro experiments showed that the cellular uptake of HMO was time-dependent. In terms of cytotoxicity, the cell survival rate of the HMO group was 65.9%, significantly lower than that of the free HMnO2 (89.1%) and MB-NO (85.1%); after 5 min of 660 nm laser irradiation, the cell survival rate of the HMO group further dropped to 48.5%. In the animal experiments of tumor xenograft models, the tumor inhibition rate of the HMO combined with 660 nm laser irradiation for 5 min group was as high as 81.3%, and it did not cause acute inflammation in the main organs, demonstrating good biological safety. In summary, the HMO nanoparticles exhibit excellent anti-tumor effects. This strategy combines CDT/PDT/gas therapy, enabling the synergistic cascade of NO/ROS/reactive nitrogen oxide species (RNOS) to promote tumor cell apoptosis and inhibit tumor growth, thereby achieving a cascading amplification of therapeutic effects and providing a treatment solution to overcome the inherent limitations of traditional photodynamic therapy.
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.
This work presents a strategy for integrating polyethylene (PE) aromatization and Friedel-Crafts-type alkylation reactions via methanol reduction for upgrading PE to 79.0 wt% liquid with 56.1 wt% aromatics at 280 °C, enabled by a bifunctional NiGa/ZSM-5-H catalyst. In the catalyst design, hierarchical ZSM-5-H promotes cracking of PE, while Ga serves not only as an active site for aromatization and alkylation but also suppresses Ni0 formation by withdrawing electron density from Ni, ensuring that Ni2+ remains as the active catalytic site for methanol reduction. Hydrogen species derived from PE aromatization participate in methanol reduction, while methanol functions mainly as a hydrogen sink and methyl donor yet also releases some hydrogen under reaction conditions, allowing the PE aromatization and methanol reduction steps to be coupled. In the presence of hydrogen, methanol reduction generates methyl species that promote Friedel-Crafts-type alkylation, incorporating methyl groups into the alkyl substituents of aromatics and boosting C9-C10 alkylaromatics yields by more than 600%. By introducing 0.99 g/g methanol equivalent into the PE-catalyst reaction system, aromatic and C9-C10 alkylaromatics yields are more than double those without methanol. The strategy is applicable to upgrading of polyolefins and offers a practical route for industrial processing of plastic waste.
To explore the application scenarios of maximum a posteriori Bayesian estimation (MAP-BE) and eXtreme Gradient Boosting (XGBoost) in the prediction of polymyxin B (PMB) exposure. Two sets of simulations based on the population pharmacokinetic (PopPK) model developed for PMB were used for the development and testing of Bayesian and XGBoost models in four scenarios. The predictive performances of MAP-BE and XGBoost for the area under the concentration-time curve (AUC) over 12-h intervals at steady state were evaluated using root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R 2). Both MAP-BE and XGBoost accurately estimate the AUC0-12h under a single sampling strategy using a dense point model (median [range] (mg·h/L): 3.32 [0.78-7.39] vs. 3.30 [0.95-7.04] for RMSE, 2.41 [0.54-5.53] vs. 2.61 [0.72-5.46] for MAE for MAP-BE and XGBoost, respectively). A single 6-h sampling strategy achieved the best prediction with negligible bias (RMSE<1 mg h/L, MAE<1 mg h/L, R 2 > 0.99). XGBoost was more accurate and efficient than MAP-BE for fitting single-trough concentrations. The performance of the 12-h XGBoost model allowed for temporal fluctuations in the 6-h range. This study provides evidence for the application scenario of MAP-BE and XGBoost for predicting the AUC of PMB, which assists in selecting better approaches when predicting drug exposure with available therapeutic drug monitoring information to guide the adjustment of dosing regimens in the clinic.